Bezpłatny fragment - Climate change impacts on different sectors of Poland

Climate change and its impact

on selected sectors in Poland

Editors: Zbigniew W. Kundzewicz

Øystein Hov

Tomasz Okruszko

Poznań 2017
©Authors

Technical editing: Iwona Pińskwar and Małgorzata Szwed

Cover design: Adam Choryński

Acknowledgments

Support of the CHASE-PL (Climate change impact assessment for selected sectors in Poland) project of the Polish-Norwegian Research Programme operated by the National Centre for Research and Development (NCBiR) under the Norwegian Financial Mechanism 2009—2014 (Norway Grants) in the frame of Project Contract No. POL-NOR/200799/90/2014 is gratefully acknowledged. Scientists collaborating in the CHASE-PL Project contributed to all chapters of this book.

The Institute of Meteorology and Water Management — National Research Institute (IMGW-PIB) is kindly acknowledged for providing meteorological and hydrological data used in the CHASE-PL project. Data received from the Institute of Meteorology and Water Management — National Research Institute were processed in the CHASE-PL project.

Authors of the book benefited of the reports of the Intergovernmental Panel on Climate Change (IPCC), as well as of the results of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the European Coordinated Downscaling Experiment Initiative (EURO-CORDEX) in the framework of the World Climate Research Programme (WCRP).

Mikołaj Piniewski is grateful for the support of the Alexander von Humboldt Foundation and of the Ministry of Science and Higher Education of the Republic of Poland. Jerzy Kozyra and Anna Nieróbca acknowledge support from the multi-year IUNG-PIB 2016—2020 project; task 7.1: Development and optimisation of assessment and forecasting (modelling) of environmental and production-economic impacts of CAP and climate change. Andrzej Ceglarz is grateful for the support of the Foundation of German Business (Stiftung der Deutschen Wirtschaft). Ilona M. Otto gratefully acknowledges funding from the Earth League’s EarthDoc Program.

Editors of this book wish to express their gratitude to principal authors of chapters, recruited among CHASE-PL scientists as well as to external scientists who were invited and generously agreed to contribute to book chapters. Polish scientists participating in the CHASE-PL project are also kindly acknowledged for translating the English chapters into Polish so that the book could be promptly, and economically, published in two language versions.

List of authors

Rasmus E. Benestad, Norwegian Meteorological Institute, Oslo, Norway; rasmusb@met.no

Andrzej Ceglarz, Potsdam Institute for Climate Impact Research, Potsdam, Germany; andrzej.ceglarz@pik-potsdam.de

Adam Choryński, Institute of Agricultural and Forest Environment of the Polish Academy of Sciences, Poznań, Poland; adam.chorynski@isrl.poznan.pl

Andreas Dobler, Norwegian Meteorological Institute, Oslo, Norway; andreas.dobler@met.no

Eirik Johan Førland, Norwegian Meteorological Institute, Oslo, Norway; eirikjf@met.no

Dariusz Graczyk, Institute of Agricultural and Forest Environment of the Polish Academy of Sciences, Poznań, Poland; darekgraczyk@wp.pl

Jan Erik Haugen, Norwegian Meteorological Institute, Oslo, Norway; janeh@met.no

Øystein Hov, Norwegian Meteorological Institute, Oslo, Norway; oystein.hov@met.no

Ignacy Kardel, Warsaw University of Life Sciences (SGGW), Warsaw, Poland; I.Kardel@levis.sggw.pl

Jerzy Kozyra, Institute of Soil Science and Plant Cultivation (IUNG) — State Research Institute, Puławy, Poland; kozyr@iung.pulawy.pl

Valentina Krysanova, Potsdam Institute for Climate Impact Research, Potsdam, Germany; krysanova@pik-potsdam.de

Zbigniew W. Kundzewicz, Institute of Agricultural and Forest Environment of the Polish Academy of Sciences, Poznań, Poland and Potsdam Institute for Climate Impact Research, Potsdam, Germany; kundzewicz@yahoo.com

Paweł Marcinkowski, Warsaw University of Life Sciences (SGGW), Warsaw, Poland; P.Marcinkowski@levis.sggw.pl

Abdelkader Mezghani, Norwegian Meteorological Institute, Oslo, Norway; abdelkaderm@met.no

Anna Nieróbca, Institute of Soil Science and Plant Cultivation (IUNG) — State Research Institute, Puławy, Poland; szewc@iung.pulawy.pl

Joanna O’Keefe, Warsaw University of Life Sciences (SGGW), Wasaw, Poland; j.okeeffe@levis.sggw.pl

Tomasz Okruszko, Warsaw University of Life Sciences (SGGW), Warsaw, Poland; T.Okruszko@levis.sggw.pl

Ilona Otto, Potsdam Institute for Climate Impact Research, Potsdam, Germany; Ilona.Otto@pik-potsdam.de

Kajsa M. Parding, Norwegian Meteorological Institute, Oslo, Norway; kajsamp@met.no

Mikołaj Piniewski, Warsaw University of Life Sciences (SGGW), Warsaw, Poland and Potsdam Institute for Climate Impact Research, Germany; M.Piniewski@levis.sggw.pl

Iwona Pińskwar, Institute of Agricultural and Forest Environment of the Polish Academy of Sciences, Poznań, Poland; iwonp1@wp.pl

Mateusz Szcześniak, Warsaw University of Life Sciences (SGGW), Warsaw, Poland; M.Szczesniak@levis.sggw.pl

Małgorzata Szwed, Institute of Agricultural and Forest Environment of the Polish Academy of Sciences, Poznan, Poland; mszwed@man.poznan.pl

Marta Utratna, Warsaw University of Life Sciences (SGGW), Warsaw, Poland; M.Utratna@levis.sggw.pl

Table of contents

Part I Setting the stage

1 Introduction

Zbigniew W. Kundzewicz, Øystein Hov and Tomasz Okruszko

The topical area of climate change and climate change impacts, recognized as very important in Norway and many countries of the European Union, does not generally get a comparable status in the public discourse in Poland. Poles are aware of climate change, but this issue is not widely considered as a priority. Observed impacts of climate change in the country are not dramatic and the attribution of these impacts is complex, in the context of multiple drivers. Combination of high natural variability of hydro-meteorological phenomena with significant uncertainty of future projection biases public discussion on these natural phenomena. The often posed question is about “believing” or “not believing” in climate change. Consequently adaptation measures are not taken as serious issue as it deserves. Mitigation policy is even more challenged. The public perception is driven by the well-rooted wisdom that Poland ‘sits on coal’. Historically, in the cumulative sense, the carbon footprint of Poland has been large and carbon dioxide emissions per unit GDP are still much higher than in most EU countries and in Norway. There is no doubt that, gradually, Poland has to decarbonize the energy sector, but the perspective of an abrupt introduction of a high carbon tax and the threat of ‘carbon leakage’, and in consequence the loss of work places in Poland towards countries that do not partake in such fiscal measures, are a reason for considerable concern across the nation. Both countries, Poland and Norway are fossil-fuel giants, with sceptics galore, yet they show different attitudes to climate change.

The present book, dealing with climate change and its impacts on selected sectors in Poland, offers a review of results of the CHASE-PL (Climate change impact assessment for selected sectors in Poland) project, carried out in 2014—2017 under the framework of the Polish — Norwegian Research Programme. The CHASE-PL project was well tuned to the overall objectives of the Programme: to contribute to reduction of economic and social disparities and to strengthen bilateral relations between Norway and Poland through financial contributions in the priority areas such as research. The CHASE-PL project aimed to provide substantial intellectual support for counteracting climate changes and their adverse impacts, hence contributing to sustainable economic development and environmental protection. Apart from the present book, published in English and in Polish, project results have also found their way as articles in fine journals rated in the ISI Thomson Reuters Web of Science.

The present book consists of five parts that, in turn, are composed of 16 chapters. Part one, setting the stage, consists of three chapters. After the present introduction come chapters two and three devoted to large-scale climate change (observations, interpretation, projections) as well as impacts of and adaptation to climate change (Kundzewicz, respectively, 2017a and 2017b).

Before impacts and risks could be tackled, the climate science harnessed in the project had to detect climate changes (part two) and to generate climate projections for the future (part three) to be used in impact oriented part four.

Part two of the book, devoted to observations of climate change in Poland, also consists of three chapters, where change detection in observed climate of Poland was examined for a range of variables of particular relevance and interest, such as temperature, precipitation and snow cover. In chapters four, five, and six, respectively, Graczyk et al. (2017a) reviewed changes in temperature, Pińskwar et al. (2017) — changes in precipitation, while Szwed et al. (2017) — changes in snow cover.

Next, projections of climate variability and change for Poland were produced and compared with the reference period. This was achieved via downscaling of General Circulation Models (GCMs) climate projections for the territory of Poland. Indeed, input of Norwegian experts was dominant in part three, dedicated to projected climate change. In chapters seven and eight, Mezghani et al. (respectively 2017a and 2017b) examined future climate changes (temperature, precipitation and snow cover) for two future time horizons and for two Representative Concentration Pattern (RCP) scenarios and discussed methodology of projections.

The book examined large-scale climate change impacts in the basins of two main rivers, the Vistula and the Odra (covering 88 % of Polish territory), where the impacts on water resources, biota, and agrosystems were considered. This is a large and pioneering task, since model based analysis for whole river basins of Vistula and Odra has not been conducted in Poland before. This was achieved in the following steps: calibration and validation of the hydrological SWAT (Soil Water Assessment Tool) model using multi-site calibration method, identification of in-stream and riparian ecosystems water needs, scenario based analysis of impact of climate change on ecosystems and agricultural production. An index-based assessment of climate change impacts was made for projections for in-stream ecosystems and wetlands. In addition, two meso-scale models, for two medium-sized lowland catchments, the Upper Narew and the Barycz (which are sub-catchments of the Vistula and the Odra basins) were calibrated and used for sediment, nitrogen and phosphorus load assessments and projections.

This is included in part four, consisting of five chapters, in which climate change impacts on sectors in Poland are tackled. In chapter nine, Piniewski et al. (2017a) discussed climate change impacts on water resources in terms of water quantity for the Vistula and the Odra rivers and water quality for the Barycz and the Upper Narew, while in chapter ten, Okruszko et al. (2017) delivered projections of climate change impact on water environment and wetlands in Poland. Two further chapters are devoted to various impacts on Poland’s agricultural sector. Kundzewicz and Kozyra (2017) discussed general climate change impact on Polish agriculture in chapter 11, while Piniewski et al. (2017b) presented model-based projections of climate change impacts on spring crops until the time horizon 2050 in chapter 12. These four chapters reveal the significant change of future abiotic conditions which may reshape the functioning of ecosystems and agrosystems on Polish territory. In the last (13th) chapter of part four, Graczyk et al. (2017b) examined observed impacts of heat waves on human mortality in large Polish towns.

Climate change and climate change impact studies would be incomplete without consideration of uncertainties that are plentiful in observations, understanding and projections. Three related issues are: identification of sources of uncertainty, quantification of components of uncertainty, and devising a framework for reducing uncertainty. The last, fifth, part of the book deals with uncertainty and perception. In chapter 14, Kundzewicz et al. (2017c) tackled uncertainty in climate change and climate change mitigation policy. Then, Kundzewicz et al. (2017a) discussed perception of climate change and its impacts in Poland and Norway in chapter 15. Finally, Kundzewicz et al. (2017b) reviewed challenges for developing national climate services in Poland and Norway.

The book, as well as the CHASE-PL project, linked strengths of both participating countries, exemplified by Norway’s traditions and achievements in climate science and Poland’s climate impact science. Norwegian experts provided common climatic foundations by producing downscaled projections, while Polish experts took the lead in impact analysis. Valuable inputs were also obtained from co-authors beyond the project.

The editors and authors of this book are confident that the presented material contributes, in a considerable way, to increase of understanding of climate change impacts in selected sectors of Poland. It extends the state-of-the-art of the detection of change, projection of climate change and its impacts, and interpretation of uncertainty.

The CHASE-PL project developed an interactive web-mapping system (climateimpact.sggw.pl) enabling other researchers to use project results in their own climate change studies, as well as filling the existing information gap on climate change impacts among the policy-makers, stakeholders and the broad Polish society. It is our strong belief that free and easy access to processed historical data and projected hydro-meteorological information allows for critical and rigid comparison of different approaches to the assessment of climate change impact. The lessons learned from such studies can help in identifying the available adaptation strategies and rising awareness of its importance. Moreover there has been a historical, disciplinary “disconnect” between communities developing integrated water cycle and water resources assessment and modelling frameworks on the one hand, and the communities developing climate modelling frameworks on the other. The CHASE-PL project made a serious attempt to bring the activities of the hydrological and climate communities closer together.

References

Graczyk D., Pińskwar I., Choryński A., Szwed M. and Kundzewicz Z.W. (2017a) Changes of air temperature in Poland. In: Climate change and its impact on selected sectors in Poland. Kundzewicz Z.W., Hov Ø., Okruszko T. (Eds.). Chapter 4, 44—56.

Graczyk D., Pińskwar I., Choryński A., Szwed M. and Kundzewicz Z.W. (2017b) Impacts of heat waves on health in large Polish towns. Ibidem. Chapter 13, 187—199.

Kundzewicz Z.W. (2017a) Large-scale climate change (observations, interpretation, projections). Ibidem. Chapter 2, 14—28.

Kundzewicz Z.W. (2017b) Climate change impacts and adaptation. Ibidem. Chapter 3, 29—42.

Kundzewicz Z.W., Kozyra J. (2017) Climate change impact on Polish agriculture. Ibidem. Chapter 11, 158—171.

Kundzewicz Z.W. Benestad R.E. and Ceglarz A. (2017a) Perception of climate change and mitigation policy in Poland and Norway. Ibidem. Chapter 15, 216—245.

Kundzewicz Z.W., Førland E.J. and Piniewski M. (2017b) Challenges for developing national climate services — Can Poland learn from Norway? Ibidem. Chapter 16, 245—255.

Kundzewicz Z.W., Hov Ø., Piniewski M., Krysanova V., Benestad R.E. and Otto, I.M. (2017c) Uncertainty in climate change and its impacts. Ibidem. Chapter 14, 201—215.

Mezghani A., Parding K.M., Dobler A., Benestad R.E., Haugen J.E. and Piniewski M. (2017a) Projections of changes in temperature, precipitation and snow cover in Poland. Ibidem. Chapter 7, 90—113.

Mezghani A., Parding K.M., Dobler A., Benestad R.E., Haugen J.E. and Kundzewicz Z.W. (2017b) Methodology of projections. Ibidem. Chapter 8, 114—123.

Okruszko T., O’Keeffe J., Utratna M., Marcinkowski P., Szcześniak M., Kardel I., Kundzewicz Z.W. and Piniewski M. (2017) Projections of climate change impact on water environment and wetlands in Poland. Ibidem. Chapter 10, 141—157.

Piniewski M., Szcześniak M., Kardel I., Marcinkowski P., Okruszko T. and Kundzewicz Z.W. (2017a) Water resources. Ibidem. Chapter 9, 125—140.

Piniewski M., Szcześniak M., Marcinkowski P., O’Keeffe J., Okruszko T., Nieróbca A., Kozyra J. and Kundzewicz Z.W. (2017a) Model-based projections of climate change impacts on spring crops until 2050. Ibidem. Chapter 12, 172—186.

Pińskwar I., Choryński A., Graczyk D., Szwed M. and Kundzewicz Z.W. (2017) Changes in precipitation in Poland. Ibidem. Chapter 5, 57—77.

Szwed M., Pińskwar I., Kundzewicz Z.W., Graczyk D. and Mezghani A. (2017) Changes in snow cover. Ibidem. Chapter 6, 78—88.

2 Large-scale climate change (observations, interpretation, projections)

Zbigniew W. Kundzewicz

2.1. The heat goes on!

The heat goes on! The year 2016 has been the warmest year on record, globally, in the history of instrumental temperature observations (since 1880). This message was announced by US government agencies — National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) as well as the World Meteorological Organization (WMO) in January 2017.

Figure 2.1 presents an estimate of global surface temperature change, determined by the NASA’s GISS Surface Temperature Analysis (GISTEMP) project, using current data files from NOAA GHCN v3 (meteorological stations), ERSST v4 (ocean areas), and SCAR (Antarctic stations), combined, as described by Hansen et al. (2010).

Fig. 2.1. Estimate of anomalies of global surface temperature change produced by the NASA’s GISS Surface Temperature Analysis (GISTEMP). Annual means and a lowess (locally weighted scatterplot smoothing) curve are shown. Anomaly refers to base interval: 1951—1980. Source: GISTEMP team (2017).

Table 2.1 presents a ranking of 20 globally warmest years in the history of observations. The global mean temperature record has been recently broken in three consecutive years, 2014, 2015, and 2016. In 2015 and 2016, the record was broken by large margins (even though the margin was even higher in the extraordinarily warm year, 1998, far above the trend, cf. Fig. 2.1). Each of the 16 individual years of the 21st century (since 2001) has been among the 17 globally warmest years on record in the history of observations. The only pre-2001 year on the list of 17 globally warmest years was 1998 (rank 9—12), coinciding with the occurrence of a strong warm phase of ENSO, i.e. El Niño.

Table 2.1. Ranking of 20 globally warmest years. Temperature anomalies [in oC] refer to base interval: 1951—1980. Source: NASA http://climate.nasa.gov/ system/internal_resources/details/original/647_Global_Temperature_Data_File. txt

In 2014—2016, a strong warm phase of the ENSO cycle, i.e. El Niño, was recorded, whose amplitude reached a very high value measured by ONI (Oceanic Niño Index), in late 2015. This strong El Niño interfered with a long-term warming trend. The ONI (Oceanic Niño Index) is based on SST (Sea Surface Temperature) departure from average in the Niño 3.4. region cf. http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/lanina/ enso_evolution-status-fcsts-web. pdf. The stages of the ENSO cycle, El Niño and La Niña, respectively, are defined if the threshold ONI ≥ 0.5 or ONI ≤ — 0.5 is exceeded by at least five consecutive overlapping 3-month seasons. Three-month average value of ONI from November 2015 to January 2016 reached 2.3, matching the highest records from October to December 1997 and from November 1997 to January 1998. The El Niño phase lasted from October-December 2014 to April-June 2016 and then the climatic system entered the ENSO-neutral phase and subsequently moved to cold La Niña conditions from July-September 2016 to November 2016 — January 2017 (over five months), with expectation of transition to ENSO-neutral phase afterwards.

Figure 2.2 presents estimates of anomalies of global temperature over land and over ocean, produced by the NASA’s GISS Surface Temperature Analysis (GISTEMP). It is clear that both land and ocean are warming, while temperature anomalies over land are usually higher than those over ocean.

Fig. 2.2. Estimates of anomalies of global temperature over land and over ocean. Anomaly refers to base interval: 1951—1980. Source: GISTEMP team (2017).

Data for the recent years, included in Figs. 2.1—2.2 and Table 2.1, update and corroborate the findings reported in the most recent, Fifth IPCC Assessment Report (AR5), http://www.ipcc.ch/report/ar5/, whose first volume, on the science of climate change, was published in 2013. The important statement made by IPCC (2013) was that the warming of the climate system of the Earth is unequivocal and that many of the observed changes are unprecedented over the time scales of decades to millennia. The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, the sea level has risen, and the concentrations of greenhouse gases have increased.

Figure 2.3 illustrates multiple observed indicators of a changing global climate system. The globally averaged combined land and ocean surface temperature data as calculated by a linear trend, show a warming of 0.85 [0.65 to 1.06] °C, over the period 1880 to 2012, when multiple independently produced datasets exist (IPCC, 2013, 2014), cf. Fig. 2.3a. The anomalies were relative to the reference period of 1986 to 2005.

However, in addition to robust multi-decadal warming, global mean surface temperature exhibits substantial decadal and inter-annual variability that renders trends based on short records very sensitive to the beginning and end dates. For instance, the warming over 1998–2012 amounting to 0.05 [–0.05 to 0.15] °C per decade only, was relatively weak. This episode was labelled as “hiatus” and “halt”. It began in a very warm year, 1998, corresponding to a strong El Niño. The vigorous warming resumed in 2014 and continued through 2015 and 2016.

The time series of decadal means of global temperature (with an estimate of decadal mean uncertainty included), presented in Fig. 2.3a show that each of the last three decades was warmer than the preceding one. Map of the observed surface temperature change from 1901 to 2012 (Fig. 2.3b) derived from temperature trends determined by linear regression from one data set (orange line in Fig. 2.3a) shows that almost the entire globe has experienced surface warming. Trends have been calculated where data availability permitted a robust estimate, other areas are left blank. Grid boxes where the trend is significant are indicated.

Continental-scale surface temperature reconstructions show, with high confidence, multi-decadal periods during the Medieval Climate Anomaly (year 950 to 1250) that were in some regions as warm as in the late 20th century, but did not occur as coherently across regions (IPCC, 2013).

Ocean warming dominates the increase in energy stored in the climate system, accounting for more than 90 % of the energy accumulated between 1971 and 2010, therein two thirds in the upper ocean (0–700 m). On a global scale, the upper 75 m of the ocean warmed by 0.11 [0.09 to 0.13] °C per decade over the period 1971 to 2010 (IPCC, 2013).

2.2. Other climatic observations

2.2.1. Shrinking cryosphere

As noted by IPCC (2013), over last decades, the Greenland and Antarctic ice sheets have been losing mass, glaciers have continued to shrink, and Arctic sea ice and Northern Hemisphere spring snow cover have continued to decrease in extent.

The average rate of ice loss from glaciers around the world, excluding glaciers on the periphery of the ice sheets, increased by more than 21 % between the periods (1971 to 2009) and (1993 to 2009).

The average rate of ice loss from the Greenland ice sheet increased nearly seven-fold between the ten-year intervals 1992 to 2001 and 2002 to 2011, while the average rate of ice loss from the Antarctic ice sheet increased nearly five-fold. This latter loss was mainly from the northern Antarctic Peninsula and the Amundsen Sea sector of West Antarctica.

Fig. 2.3. Multiple observed indicators of a changing global climate system. (a) Observed globally averaged combined land and ocean surface temperature anomalies (relative to the mean of 1986 to 2005 period, as annual and decadal averages) with an estimate of decadal mean uncertainty included for one data set (grey shading). (b) Map of the observed surface temperature change, from 1901 to 2012, derived from temperature trends determined by linear regression from one data set (orange line in Panel a). Trends have been calculated where data availability permitted a robust estimate (i.e., only for grid boxes with greater than 70 % complete records and more than 20 % data availability in the first and last 10 % of the time period), other areas are white. Grid boxes where the trend is significant, at the 10 % level, are indicated by a + sign. (c) Arctic (July to September average) and Antarctic (February) sea ice extent. (d) Global mean sea level relative to the 1986–2005 mean of the longest running data set, and with all data sets aligned to have the same value in 1993, the first year of satellite altimetry data. All time series (coloured lines indicating different data sets) show annual values, and where assessed, uncertainties are indicated by coloured shading. (e) Map of observed precipitation change, from 1951 to 2010; trends in annual accumulation calculated using the same criteria as in Panel b. Source: IPCC (2014a).

The annual mean Arctic sea ice extent decreased over the period 1979 to 2012 with a rate in the range 3.5 to 4.1 % per decade and 9.4 to 13.6 % per decade for the summer sea ice minimum (perennial sea ice), while the annual mean Antarctic sea ice extent increased (sic!) at a rate in the range of 1.2 to 1.8 % per decade. Figure 2.3c illustrates Arctic (July to September average) and Antarctic (February) sea ice extent.

The extent of Northern Hemisphere snow cover has decreased and permafrost temperatures have increased in most regions. In the Russian European North, a considerable reduction in permafrost thickness and areal extent has been observed.

2.2.2. Sea level

Figure 2.3d presents global mean sea level relative to 1986–2005. The rate of sea level rise (SLR) since the mid-19th century, reported in IPCC (2013), has been larger than the mean rate during the previous two millennia. There was a transition in the late 19th to the early 20th century from relatively low mean rates of rise to higher rates. Over the period 1901 to 2010, global mean sea level rise (SLR) was appr. 0.19 m, that is, the mean rate of global averaged SLR was 1.7 mmyr–1 between 1901 and 2010. The SLR clearly advanced more recently, reaching 2.0 mmyr–1 between 1971 and 2010 and 3.2 mmyr–1 between 1993 and 2010.

Over the interval of 1993—2010, global mean SLR was broadly consistent with the sum of the observed contributions from thermal expansion of ocean water due to warming (1.1 mmyr–1), changes in glaciers (0.76 mmyr–1), Greenland ice sheet (0.33 mmyr–1), Antarctic ice sheet (0.27 mmyr–1), and land water storage (0.38 mmyr–1). The sum of these contributions slightly exceeds 2.8 mmyr–1 (in comparison to the estimate of 3.2 mmyr–1 given above). However, there is still considerable uncertainty.

2.2.3. Precipitation and extremes

Figure 2.3e presents a global map of observed precipitation change, from 1951 to 2010. Averaged over the mid-latitude land areas of the Northern Hemisphere, precipitation has increased since 1901. Confidence of this statement is regarded as medium before 1951 and high afterwards.

Changes in many extreme weather and climate events have been observed. Warm extremes (e.g. number of warm days and nights, frequency of heat waves) are on the rise while cold extremes (e.g. number of cold days and nights) are on the decrease. The frequency or intensity of heavy precipitation events has likely increased in North America and Europe.

2.2.4. Carbon and other biogeochemical cycles

The iconic, 59-year time series of observations of atmospheric concentrations of carbon dioxide, carried out at Mauna Loa (Hawaii, USA) shows a steady, seasonally modulated, increase (Fig. 2.4). Seasonal cycle, within any one year, corresponds to seasonal development of phases of vegetation. The series of observations at Mauna Loa is the longest high-quality time series of atmospheric CO2 concentrations, worldwide, collected since March 1958. The most recent monthly value, determined for January 2017 was 406.13 ppm, i.e. by 3.61 ppm higher than in January 2017 (402.52 ppm).

As summarized by IPCC (2013, 2014, 2014a), the atmospheric concentrations of the greenhouse gases: carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) have all largely increased since 1750. In 2011, the global average concentrations of these greenhouse gases were 391 ppm (parts per million), 1803 ppb (parts per billion, 1 billion = 1,000 million), and 324 ppb, respectively, and exceeded the pre-industrial levels by about 40 %, 150 %, and 20 %.

Fig. 2.4. Observations of atmospheric concentrations of carbon dioxide at Mauna Loa. (a) Recent record, since 2013. (b) Complete record, since 1958. Source: NOAA. https://www.esrl.noaa.gov/gmd/ccgg/trends/

Concentrations of CO2, CH4, and N2O now substantially exceed the highest concentrations ever recorded in ice cores during the past 800,000 years. The mean rates of increase in atmospheric concentrations over the past century are unprecedented in the last 22,000 years.

Enriched CO2 in the atmosphere leads to ocean acidification that is quantified by decrease in pH of ocean surface water by 0.1 since the beginning of the industrial era, corresponding to a 26 % increase in hydrogen ion concentration.

2.3. Drivers of climate change

Natural and anthropogenic substances and processes that alter the Earth’s energy budget are drivers of climate change. As summarized by IPCC (2013), the total natural radiative forcing, RF, from solar irradiance changes and stratospheric volcanic aerosols has made only a small contribution to the net radiative forcing, except for brief periods after large volcanic eruptions.

The RF quantifies the change in energy fluxes caused by changes in these drivers. Positive RF forcing leads to warming, while negative RF forcing leads to cooling. The best estimate for the total anthropogenic RF for 2011 relative to 1750 is 2.29 Wm−2, and it has increased more rapidly since 1970 than during prior decades. The RF from changes in concentrations in well-mixed greenhouse gases (CO2, CH4, N2O, and Halocarbons) is 2.83 Wm−2, while emissions of CO2 alone and of CH4 alone have caused an RF of 1.68 Wm−2 and 0.97 Wm−2, respectively (IPCC, 2013).

The RF of the total aerosol effect in the atmosphere, which includes cloud adjustments due to aerosols, is negative, –0.9 Wm−2. It is a net result of a negative forcing from most aerosols and a positive contribution from black carbon absorption of solar radiation. Aerosols and their interactions with clouds have offset a substantial portion of global mean forcing from well-mixed greenhouse gases and continue to contribute the largest uncertainty to the total RF estimate.

2.4. Understanding the climate system

Understanding recent changes in the climate system results from combining observations, studies of feedback processes, and model simulations. With time, we have more detailed and longer observations and improved climate models. The most important climate-change attribution statement has been subject to considerable evolution in course of five consecutive IPCC assessment reports (1990—2013). The most recent, Fifth IPCC Assessment Report (IPCC, 2013) contains the strongest attribution statement of all the IPCC reports. It states ”It is extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together”. The qualifier „extremely likely” was defined to correspond to the probability in excess of 95 %.

2.4.1. Climate models have improved

Human influence on the climate system is clear. This is evident from the increasing greenhouse gas concentrations in the atmosphere, positive radiative forcing, observed warming, and understanding of the climate system.

Climate models have improved with time and now they reproduce observed global- and continental-scale surface temperature patterns and trends over many decades, including the more rapid warming since the mid-20th century and the cooling immediately following large volcanic eruptions.

The long-term climate model simulations show a trend in global-mean surface temperature from 1951 to 2012 that agrees with the observed trend even if there are differences between simulated and observed trends over periods (e.g., 1998 to 2012).

The observed reduction in surface warming trend over the period 1998 to 2012 as compared to the period 1951 to 2012, is due to a reduced trend in radiative forcing and a cooling contribution from natural internal variability, which includes a possible redistribution of heat within the ocean.

2.4.2. Quantification of climate system responses

Observational and model studies of temperature change, climate feedbacks and changes in the Earth’s energy budget together provide confidence in the magnitude of global warming in response to the sum of forcings. The net feedback from the combined effect of changes in water vapour, and differences between atmospheric and surface warming is positive and therefore amplifies changes in climate. The net radiative feedback due to all cloud types combined is likely positive, with uncertainty in the impact of warming on low clouds.

Greenhouse gases contributed a global mean surface warming likely to be in the range of 0.5°C to 1.3°C over the period 1951 to 2010, with the contributions from other anthropogenic forcings, including the cooling effect of aerosols, likely to be in the range of −0.6°C to 0.1°C. The contribution from natural forcings is likely to be in the range of −0.1°C to 0.1°C, and from natural internal variability is likely to be also in the range of −0.1°C to 0.1°C. Together these assessed contributions are consistent with the observed warming of approximately 0.6°C to 0.7°C over this period (IPCC, 2013).

Not only warming of the atmosphere and the ocean have been attributed to human influence, but also changes in the global water cycle, reductions in snow and ice, global mean sea level rise, and changes in some climate extremes.

It is very likely (IPCC, 2013) that anthropogenic forcings have made a substantial contribution to increases in global upper ocean heat content (0–700 m) and have affected the global water cycle (observed increases in atmospheric moisture content, global-scale changes in precipitation patterns over land, to intensification of heavy precipitation over land, and changes in surface and sub-surface ocean salinity).

2.4.3. Data-mining approach

Interpretation of changes of global temperature is important for understanding of the climate system and for confidence in projections for the future. Massive efforts have been devoted over last decades to improve the accuracy of reproducing the global temperature by the climate models, but still the hindcasts are not accurate. Notwithstanding the need to further advance climate models, one may consider data-driven approaches that are capable of providing practically useful results in a much simpler and faster way (Stanisławska et al., 2013). Without assuming any prior knowledge about the hopelessly complex physics of the process and without imposing a model structure that encapsulates the existing knowledge, Stanisławska et al. (2013) managed to hindcast the global temperature by automatically identified evolutionary computation models (data-mining approach). Records of global temperature and climate drivers over 60 years were used, with training and testing periods being 1950—1999 and 2000—2009, respectively. Stanisławska et al. (2013) demonstrated that the global temperature observed in the past can be mimicked by evolutionary computation with reasonably good accuracy.

By removing CO2 concentration from data set (jack-knifing) significantly deteriorated results were obtained by Stanisławska et al. (2013). Figure 2.5 illustrates hindcasts generated by models devoid of CO2 from the data set. After removing this variable from the data set, the model can still provide a reasonably good fit for the training interval, yet it does not perform well for the test interval. This illustrates the phenomenon of overfitting, indicating that the removed variable does really matter. In absence of CO2, the models simply cannot reconstruct the global temperature of 2000s.

Fig. 2.5. Effect of removing atmospheric CO2 concentration from the data set. Interpretation of axes:

x — years, y — values of temperature anomalies (black — observed, red — simulated, with atmospheric CO2 concentration removed).

2.5. Projections for the future

2.5.1. Scenarios

A new set of future scenarios, denoted Representative Concentration Pathways (RCPs), cf. Moss et al. (2010); Meinshausen et al. (2011), was used in the present work. The RCPs are identified by their approximate total radiative forcing in year 2100 relative to 1750: 2.6 Wm-2 for RCP2.6, 4.5 Wm-2 for RCP4.5, 6.0 Wm-2 for RCP6.0, and 8.5 Wm-2 for RCP8.5. These four RCPs include one mitigation scenario leading to a very low forcing level (RCP2.6), two stabilization scenarios (RCP4.5 and RCP6), and one scenario with very high greenhouse gas emissions (RCP8.5). The RCPs can thus represent a range of climate policies, as compared with the no-climate policy of the scenarios used earlier. Also in the further chapters of this book, the RCPs (RCP4.5 and/or RCP8.5) are being used.

The new scenarios were used for the new climate model simulations carried out under the framework of the Coupled Model Intercomparison Project Phase 5 (CMIP5) of the World Climate Research Programme (WCRP). In all RCPs, atmospheric CO2 concentrations are higher in 2100 relative to present day as a result of a further increase of cumulative atmospheric GHG emissions during the 21st century.

2.5.2. Climate projections

Models simulate climate changes based on a set of scenarios of anthropogenic forcings, indicating that continued emissions of greenhouse gases will cause further warming and changes in all components of the climate system. Substantial and sustained reductions of greenhouse gas emissions will be required to limit climate change.

The global mean surface temperature change for the period 2016–2035 relative to 1986–2005 will likely be in the range of 0.3°C to 0.7°C, assuming that there will be no major volcanic eruptions or secular changes in total solar irradiance, while for 2081–2100 is projected to likely be in the ranges 0.3°C to 1.7°C (RCP2.6), 1.1°C to 2.6°C (RCP4.5), 1.4°C to 3.1°C (RCP6.0), 2.6°C to 4.8°C (RCP8.5). The Arctic region will warm more rapidly than the global mean and warming over land will be larger than over the ocean (IPCC, 2013).

Figure 2.6a illustrates multi-model mean projections of change in annual mean surface temperature for the 2081–2100 period under the RCP2.6 (left) and RCP8.5 (right) scenarios.

Relative to the average from year 1850 to 1900, global surface temperature change by the end of the 21st century is projected to likely exceed 1.5°C for RCP4.5, RCP6.0 and RCP8.5, i.e. for all RCP scenarios except RCP2.6. Warming is likely to exceed 2°C for RCP6.0 and RCP8.5, but unlikely to exceed 2°C for RCP2.6. Warming is unlikely to exceed 4°C for RCP2.6, RCP4.5 and RCP6.0 (IPCC, 2013). Warming will continue to exhibit interannual-to-decadal variability and will not be regionally uniform.

The ocean will continue to warm during the 21st century. Heat will penetrate from the surface to the deep ocean and affect ocean circulation. Best estimates of ocean warming in the top 100 meters are about 0.6°C (RCP2.6) to 2.0°C (RCP8.5).

The Arctic sea ice cover will continue to shrink and thin and Northern Hemisphere spring snow cover will decrease during the 21st century as global mean surface temperature rises and global glacier volume will further decrease.

Global mean sea level will continue to rise, with increasing rate of SLR, due to increased ocean warming and increased loss of mass from glaciers and ice sheets. Global mean SLR for 2081–2100 relative to 1986–2005 will likely be in the ranges of 0.26 to 0.55 m for RCP2.6, 0.32 to 0.63 m for RCP4.5, 0.33 to 0.63 m for RCP6.0, and 0.45 to 0.82 m for RCP8.5 (IPCC, 2013). Figure 2.6c illustrates multi-model mean projections of change in annual sea level, for the 2081–2100 period under the RCP2.6 (left) and RCP8.5 (right) scenarios.

Fig. 2.6. Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model mean projections (i.e., the average of the model projections available) for the 2081–2100 period under the RCP2.6 (left) and RCP8.5 (right) scenarios for (a) change in annual mean surface temperature and (b) change in annual mean precipitation, in percentages, and (c) change in average sea level. Changes are shown relative to the 1986–2005 period. The number of CMIP5 models used to calculate the multi-model mean is indicated in the upper right corner of each panel. Stippling (dots) on (a) and (b) indicates regions where the projected change is large compared to natural internal variability (i.e., greater than two standard deviations of internal variability in 20-year means) and where 90 % of the models agree on the sign of change. Hatching (diagonal lines) on (a) and (b) shows regions where the projected change is less than one standard deviation of natural internal variability in 20-year means. Source: IPCC (2014a).

Changes in the global water cycle will not be uniform. Figure 2.6b illustrates multi-model mean projections of change in annual mean precipitation, in percentages, for the 2081–2100 period under the RCP2.6 (left) and RCP8.5 (right) scenarios. The contrast in precipitation between wet and dry regions and between wet and dry seasons will increase (dry getting drier and wet getting wetter). Extreme precipitation events are projected to become more intense and more frequent.

Climate change will affect carbon cycle processes in a way that will exacerbate the increase of CO2 in the atmosphere. In consequence, further uptake of carbon by the ocean will increase ocean acidification. Cumulative emissions of CO2 will largely determine global mean surface warming. Most aspects of climate change will persist for many centuries even if emissions of CO2 are stopped — there is a substantial multi-century climate change commitment created by past and present emissions of CO2. In order to fulfil the objectives of the UNFCCC Paris Agreement, global decarbonization is needed and reduction of GHG emissions that should attain net negative values in the second half of the 21st century.

2.6. Final remarks

The warming of the climate system of the Earth, observed in recent decades is unabated and unequivocal. The decade of 2000s was globally warmer than the decade of 1990s, the 1990s — warmer than 1980s, and the 1980s — warmer than the 1970s. Each of the 16 individual years of the 21st century has been among the 17 globally warmest years in the history of global temperature observations. The global mean temperature records have been recently broken in three consecutive years, 2015, 2015, and 2016. Many of the observed changes are unprecedented over the time scales of decades to millennia. The attribution of the ongoing climate change is clear — more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by anthropogenic forcings, and increase in anthropogenic greenhouse gas concentrations in particular. Projections for the future indicate further, ubiquitous, warming, but its rate can be influenced by human actions. Climate change is not only limited to increasing temperature and related effects (shrinking cryosphere and sea level rise), but includes many other changes in the climate system.

References

GISTEMP Team (2017) GISS Surface Temperature Analysis (GISTEMP). NASA Goddard Institute for Space Studies. Dataset accessed 20YY-MM-DD at https://data.giss.nasa.gov/gistemp/.

Hansen J., Ruedy R., Sato M., Lo K. (2010) Global surface temperature change, Rev. Geophys. 48, RG4004.

IPCC (2013) Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker T.F., Qin D., Plattner G.-K., Tignor M., Allen S.K., Boschung J., Nauels A., Xia Y., Bex V. and Midgley P.M. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

IPCC (2014) Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field C.B., Barros V.R., Mastrandrea M.D. and Mach K.J. (eds.)] Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

IPCC (2014a) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

Kundzewicz Z.W. (2014) Fifth IPCC Assessment Report now out. Papers on Global Change 21, 9–27.

Meinshausen M., Smith S. J., Calvin K., Daniel J. S., Kainuma M.L.T., Lamarque J-F., Matsumoto K., Montzka S.A., Raper S.C.B.,, Riahi K., Thomson A., Velders G.J.M. and van Vuuren D.P.P. (2011) The RCP greenhouse gas concentrations and their extension from 1765 to 2300. Climatic Change, 109(1–2), 213–241, DOI: 10.1007/s10584—011-0156-z

Moss R.H., Edmonds J.A., Hibbard K.A., Manning M.R., Rose S.K., van Vuuren D.P., Carter T.R., Emori S., Kainuma M., Kram T., Meehl G.A., Mitchell J.F.B., Nakicenovic N., Riahi K., Smith S.J., Stouffer R.J., Thomson A.M., Weyant J.P. and Wilbanks T.J. (2010) The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747–756, DOI:10.1038/nature 08823.

Stanisławska K., Kundzewicz Z.W. and Krawiec K. (2013) Hindcasting global temperature by evolutionary computation.Acta Geoph. 61(3), 732—751, DOI: 10.2478/s11600—012-0091-z.

3 Climate change impacts and adaptation

Zbigniew W. Kundzewicz
Introduction

There has been an increasing body of evidence of the ongoing warming at the global, continental, and sub-continental (national, regional, local) scales (IPCC, 2013). A discernible and attributable warming, due to the rising anthropogenic greenhouse gas concentrations, has been already observed and its rate has recently accelerated. Beside temperature, climate change pertains to other variables in the climatic system that, collectively, have considerable impacts on multiple natural and human systems. This chapter, strongly drawing from IPCC (2014), reviews major risks and reasons for concern, as well as impacts on selected sectors.

Observed changes

Climate change impacts have been already observed in many systems, sectors and regions. For instance, increasing temperature and changing precipitation have already altered hydrological systems and water resources. Many species of fauna and flora have shifted their geographic ranges (e.g. tree lines), seasonal activities, migration patterns, abundances, and species interactions (e.g. predator-prey) in response to ongoing climate change. Impacts of climate change on agricultural crop yields have been noted as well. Patterns of pests and diseases, vectors and hosts, have changed. Climate change has negatively affected wheat and maize yields for many regions and in the global aggregate. Several periods of rapid food and cereal price increases following climate extremes in key producing regions as well as biofuel crops replacing food crops indicate a sensitivity of food markets.

Differences in vulnerability and exposure to extreme meteorological and hydrological events arise from non-climatic factors and from inequalities resulting from uneven development processes that shape differential risks from climate change. Impacts from recent climate-related extremes, such as heat waves, droughts, floods, cyclones, and wildfires, reveal significant vulnerability and exposure of some ecosystems and many human systems (in many dimensions, including health and wellbeing) to the existing climate variability. Climate-related hazards definitely exacerbate other stressors, often with adverse effects for human livelihoods, especially for people living in poverty.

Despite increasing knowledge, uncertainties about future impacts, vulnerability, exposure, and responses of interlinked human and natural systems continue to be large.

Key risks

There are several categories of key risks that span across sectors and regions (after IPCC, 2014, modified):

Risk of death, injury, ill-health, or disrupted livelihoods in low-lying coastal zones and small islands, due to storm surges, coastal flooding, and sea-level rise, as well as for large urban populations due to inland flooding.

Systemic risks due to extreme weather events (e.g. floods, droughts, heat waves, cold waves, gale winds) leading to breakdown of infrastructure networks and critical services such as water supply, electricity, as well as health and emergency services.

Risk of mortality and morbidity during heat waves, particularly for vulnerable urban populations and people working outdoors.

Risk of food insecurity and the breakdown of food systems linked to warming and hydrological extremes, particularly for poorer populations.

Risk of loss of rural livelihoods and income due to insufficient access to drinking and irrigation water and reduced agricultural productivity, particularly in less developed semi-arid regions.

Risk of loss of marine, coastal, terrestrial and inland water ecosystems, biodiversity, and the ecosystem goods, functions, and services they provide for livelihoods.

Many key risks constitute difficult challenges for the least developed countries and vulnerable communities, given their limited ability to cope with hazards.

Reasons for concern

Five integrative reasons for concern (Fig. 3.1) summarize key risks across sectors and regions, illustrating the implications of climate change (scaled by warming) and of adaptation limits for people, economies, and ecosystems, in the form of the so-called burning ember diagram (IPCC, 2014). All temperatures are given as global average temperature change relative to the reference interval 1986—2005 (understood as “recent”). Figure 3.1 illustrates what can be regarded as “dangerous anthropogenic interference”, according to the Article 2 of the United Nations Framework Convention on Climate Change (UNFCCC). The burning ember diagram is juxtaposed to temperature projections.

Unique and threatened systems: Some unique and threatened systems, including ecosystems and cultures, are already at risk from the ongoing climate change. The number of such systems at risk of severe consequences is higher with additional warming. Many species and systems with limited adaptive capacity are subject to very high risks with additional warming of 2°C or higher, particularly Arctic sea ice and coral reef systems.

Extreme weather events: Climate-change-related risks from extreme events, such as heat waves, extreme precipitation, and coastal flooding, are already moderate and will be high already with 1°C additional warming. Risks associated with some types of extreme events (e.g., extreme heat) increase further at higher temperatures.

Distribution of impacts: Risks are unevenly distributed in space and are generally greater for disadvantaged people and communities. There are already regionally differentiated climate-change impacts on crop production. Based on projected decreases in regional crop yields and water availability, risks of unevenly distributed impacts are high for additional warming above 2°C.

Aggregate global impacts: Risks of aggregate global impacts are moderate for additional warming between 1—2°C, reflecting impacts to both Earth’s biodiversity and the overall global economy. Extensive biodiversity loss with associated loss of ecosystem services results in high risks around 3°C additional warming. Aggregate economic damages would accelerate for additional warming around 3°C or above.

Large-scale singular events: With increasing warming, some physical systems or ecosystems may be at risk of abrupt and irreversible changes that is moderate between 0—1°C additional warming (e.g. irreversible regime shifts in warm-water coral reef and Arctic ecosystems). Risks increase disproportionately as temperature increases further and become high above 3°C. For sustained warming greater than some threshold (tipping point), near-complete loss of the Greenland ice sheet would occur over a millennium or more, contributing up to 7 m of global sea-level rise.

Fig. 3.1. A global perspective on climate-related risks. Risks associated with reasons for concern are shown at right for increasing levels of climate change. The color shading indicates the additional risk due to climate change when a temperature level is reached and then sustained or exceeded. Undetectable risk (white) indicates no associated impacts are detectable and attributable to climate change. Moderate risk (yellow) indicates that associated impacts are both detectable and attributable to climate change with at least medium confidence, also accounting for the other specific criteria for key risks. High risk (red) indicates severe and widespread impacts, also accounting for the other specific criteria for key risks. Purple, introduced in IPCC AR5 assessment, shows that very high risk is indicated by all specific criteria for key risks. For reference, past and projected global annual average surface temperature is shown at left (as in IPCC, 2013). Source: (IPCC, 2014).

Increasing magnitudes of warming increase the likelihood of severe, pervasive, and irreversible impacts. The overall risks of climate change impacts can be reduced by effective global climate change mitigation policy, limiting the rate and magnitude of climate change.

Sectors and systems

Projections for the future illustrate that risks of climate change increase significantly with increasing atmospheric greenhouse gas concentrations. The projected impact on water resources is serious (Fig. 3.2).

Fig. 3.2. Percentage change of mean annual streamflow for a global mean temperature rise of 2°C above 1980–2010 (2.7°C above pre-industrial). Color hues show the multi-model mean change across 4 GCMs and 11 global hydrological models (GHMs), and saturation shows the agreement on the sign of change across all GHM-GCM combinations (percentage of model runs agreeing on the sign of change) (Schewe et al., 2013). Source: Jimenez et al. (2014).

The fraction of global human population experiencing water scarcity and the fraction often affected by destructive water abundance (major river floods) is projected to increase with the level of warming. Climate change is projected to reduce renewable surface water and groundwater resources in most dry subtropical regions, intensifying competition for water among sectors. In presently dry regions, drought frequency will increase whilst in contrast, water resources will increase at high latitudes — in brief, dry regions are projected to get drier and wet to get wetter. There are bad projections for the Mediterranean region, much of Southern USA, South Africa, South America and Australia are likely to become warmer and drier, hence streamflows decrease (Fig. 3.2). In contrast, streamflows will likely increase in much of Siberia, North Canada and Alaska, as well as Indian Peninsula.

A large fraction of both terrestrial and freshwater species of flora and fauna face increased risk of extinction under projected climate change, especially as climate change interacts with other stressors, such as habitat modification, over-exploitation, pollution, and invasive species. Within 21st century, magnitudes and rates of climate change associated with medium- to high-emission scenarios pose considerable risk of abrupt and irreversible change in the composition, structure, and function of terrestrial and freshwater ecosystems, including wetlands, at the regional scale.

Due to sea-level rise, coastal systems and low-lying coastal areas will increasingly experience adverse impacts such as submergence, coastal flooding, and coastal erosion. Due to projected climate change, global marine species redistribution and marine biodiversity reduction in sensitive regions will occur, challenging the sustained provision of productivity of fisheries and other ecosystem services. For medium- to high-emission scenarios, ocean acidification poses substantial risks to marine ecosystems, especially coral reefs and polar ecosystems, associated with impacts on the physiology, behavior, and population dynamics of individual species from phytoplankton to animals.

For the major crops (wheat, rice, and maize) in tropical and temperate regions, climate change without adaptation is projected to negatively impact production for local temperature increases of 2°C or more above late-20th-century levels, although individual locations (e.g. in the North) may benefit of such a warming.

Summary of estimates of the impact of recent climate trends on yields for major crops, reported in studies for China, India, United States, France, Scotland, Australia, Russia, as well as studies for multiple countries or global aggregates, taken from the peer-reviewed literature is given in Porter et al. (2014). Decreases of yield are dominant (median and most inter-quartile ranges).

Many global risks of climate change are concentrated in urban areas, where majority of the global population live now. By 2050, the number of inhabitants of urban areas is expected to increase to 64–69 % of the global population. Major future rural impacts are expected through impacts on water availability and supply, food security, and agricultural incomes, including shifts in production areas of crops across the world.

Projected climate change will impact human health by exacerbating health problems that already exist, but will also lead to increases in ill-health in many regions and especially in developing countries with low income, as compared to a baseline without climate change. In particular, heat waves and infectious diseases are projected to intensify with adverse consequences to human health.

There are complex, multi-sectoral impacts of climate change (and on climate change), such as the water-energy-food nexus illustrated in Figure 3.3. This figure illustrates bilateral interlinkages of water and energy, water and agriculture, as well as energy and agriculture.

Fig. 3.3. The water-energy-food nexus as related to climate change. The interlinkages of supply/demand, quality and quantity of water, energy and food/feed/fiber with changing climatic conditions have implications for both adaptation and mitigation strategies. Source: Arent et al. (2014).

Global economic impacts from climate change are difficult to estimate. Estimates vary in their coverage of subsets of economic sectors and depend on a large number of assumptions, many of which are disputable. With these recognized limitations, the incomplete estimates of global annual economic losses for additional temperature increases of ~2°C are between 0.2 and 2.0 % of income. Additionally, there are large differences between and within countries. Losses accelerate with greater warming. There is a large spread among estimates of the incremental economic impact of emitting carbon dioxide — from a few US dollars to several hundreds of US dollars per ton of carbon. Estimates vary strongly with the assumed damage function and discount rate.

For most economic sectors, the impacts of non-climatic drivers (e.g. changes in population, age structure, income, technology, relative prices, lifestyle, regulation, and governance) are projected to be large relative to the impacts of climate change.

Climate change over the 21st century is projected to increase displacement of people and can indirectly increase risks of violent conflicts by amplifying well-documented drivers of these conflicts such as poverty and economic shocks.

The impacts of climate change on the critical infrastructure and territorial integrity of many states are projected to likely influence national security. For example, land inundation due to sea-level rise poses risks to the territorial integrity of small-island states and states with extensive coastlines. Some transboundary impacts of climate change, such as changes in sea ice, shared water resources, and pelagic fish stocks, have the potential to increase rivalry among states.

Throughout the 21st century, climate-change impacts are projected to slow down economic growth, make poverty reduction more difficult, adversely affect food security, and prolong existing and create new poverty traps, the latter particularly in urban areas and emerging hotspots of hunger.

In Europe, there is a latitudinal divide of agricultural impacts. The north gets warmer and wetter, hence the change is advantageous from the agriculture viewpoint, while the south gets warmer and drier and this is a bad news for agriculture (Fig. 3.4). Tourism is projected to thrive in the north and suffer in the south, where summers are going to be too hot, so that the summer holiday destinations are likely to change. Length of ski season in Europe is projected to decrease. Hydropower will likely decrease in all regions, also power plant cooling requirements may not be met during hot and dry episodes. Transport problems may include heat impact on rail and drought impact on inland navigation. Provision of ecosystem services is projected to decrease.

Adaptation, managing risks and building resilience

A working definition of adaptation may read: adjustment in response to observed or expected changes, which moderates harm or exploits beneficial opportunities. Throughout history, people and societies have always adjusted to and coped with climate, climate variability, and extremes, with varying degrees of success. Adaptation to climate change is now becoming embedded in planning processes, with more limited implementation of responses. Adaptation experience is accumulating. Governments at various levels are starting to develop adaptation plans and policies and to integrate climate-change considerations into broader development plans.

Taxonomy of adaptation distinguishes several classification categories, e.g.: anticipatory adaptation (proactive; adaptation to projected changes) or reactive measures (adaptation to past or ongoing changes); autonomous (spontaneous) or planned; as well as private or public. Adaptation to climate change can be implemented via spatial / land use planning, structural / physical approaches, institutional, disaster risk management and ecosystem management.

Fig. 3.4. Percentage change in simulated water-limited yield for winter wheat in 2030 with respect to the 2000 baseline for the A1B scenario using ECHAM5 (left column) and HadCM3 (right) GCMs. Upper maps to do not take adaptation into account. Bottom maps include adaptation. After: Donatelli et al. (2012). Source: Kovats et al. (2014).

Capacity to adapt to climate change impacts varies across regions, societies and income groups. These differences reflect such factors as wealth, housing quality and location, level of education, mobility etc. However, enhancing adaptive capacity, i.e. increasing system’s coping capacity and coping range, is ubiquitously needed. Adaptation policy stakeholders (persons or organizations that have a legitimate interest in a project or policy, or would be affected by a particular action or policy) are manifold, from central via regional to local authorities, individuals and communities affected, planning bodies, NGOs, researchers and the media. Participatory decision making is indispensable in the adaptation process. Supra-national bodies (such as the European Union) and national governments are expected to create enabling and enhancing environment.

There exist several categories of limits to adaptation, therein physical limits (e.g., when rivers dry up completely, hence adverse effects on the water sector cannot be avoided); economic limits (affordability; or cost — benefit / and cost — efficiency concerns); socio-political limits (e.g., constructing a structural defense may not be acceptable e.g. due to the detrimental effects to the environment and the need for resettlement of people); or institutional limits (e.g. inadequate capacity of institutions). Barriers to adaptation to floods via relocation can be physical, e.g. lack of land for relocation, but also social — unwillingness of people to relocate.

Water management decisions have always been made on the basis of uncertain information. Yet, climate change challenges the existing water management practices by adding considerable uncertainties and novel risks that can be outside the range of experience. Adaptation, both reactive and anticipative, makes use of a feedback mechanism, implementing modifications (and possibly correcting past mistakes) in response to acquisition of new knowledge and information (from monitoring and research, e.g. modelling studies producing scenarios).

Man-made systems are traditionally designed and operated on the basis of the stationarity assumption: the past is the key to the future. However, in fact, „the stationarity is dead” (Milly et al., 2008, 2015), hence the existing stationarity-based design procedures are unlikely to be optimal: systems can be under- or over-designed resulting in either inadequate performance or excessive costs (e.g., with large safety margin).

Unfortunately, the existing climate projections for the future are loaded with high uncertainty. Hence the question may arise — adapting to what? Clearly, uncertainty in climate impact projections has implications for adaptation practices. Adaptation procedures need to be developed, which do not rely on precise projections of changes. Managers cannot have confidence in an individual scenario or a crisp projection for the future.

There are a range of adaptation measures. One can try to prevent the adverse effects of climate change by structural and technological means (e.g. hard engineering solutions and implementation of improved design standards), or by legislative, regulatory and institutional means (integrated management; revision of guidance notes for planners and design standards). One can avoid or reduce risk by relocation or other avoidance strategy, improvement in forecasting systems, contingency and disaster plans. One can share loss (insurance-type strategies) and be prepared to take residual risk. Research (reducing uncertainties), education, and awareness raising are essential pre-requisites for adaptation to climate change.

From the sustainable development perspective, the adaptation should reduce the vulnerabilities of people and societies to shifts in climate variables, increased climate variability, and extreme events, and should protect and restore ecosystems that provide critical land and water resources and services.

Planning horizons and life times for some adaptation options (e.g., dams, forests) are up to many decades, during which information is expected to change. There is an opportunity cost of failure to act early vs value of delay (narrower range of uncertainty) and a controversy whether to adapt now to the existing uncertain projections or to wait for better information and adapt then. Early adaptation is effective, provided that projections of future climate change are sufficiently accurate, while delayed adaptation may lead to greater subsequent costs. Then, precautionarity principle should offer guidance. For impacts where confidence in the projections is high (e.g. ubiquitous warming), adaptation should start early.

There exist „no-regret” strategies: do things that make sense anyway. It is always good to save energy, water, and raw materials. Improved incorporation of current climate variability into management would render societies better prepared to future climate change. However, adaptation to climate change impacts typically entails significant expenditures that can be estimated from bottom-up sector-specific studies. However, adaptation cost estimates are often speculative. Even less is known about the benefits of adaptation, in terms of damages avoided.

Responding to climate-related risks involves decision-making in a changing world, with continuing uncertainty about the severity and timing of climate-change impacts and with limits to the effectiveness of adaptation. Adaptation and mitigation choices in the near-term will affect the risks of climate change throughout the 21st century. Adaptation is place and context specific, with no single approach for reducing risks appropriate across all settings.

Adaptation planning and implementation can be enhanced through complementary actions across levels, from individuals to governments.

A first step towards adaptation to future climate change is reducing vulnerability and exposure to present climate variability and improving livelihood security. Strategies include actions with co-benefits for other objectives. Adaptation planning and implementation are contingent on societal values, objectives, and risk perceptions. Poor planning, overemphasizing short-term outcomes, or failing to sufficiently anticipate consequences can result in maladaptation.

Significant co-benefits, synergies, and tradeoffs exist between mitigation and adaptation and among different adaptation responses. Prospects for climate-resilient pathways for sustainable development are related fundamentally to what the world accomplishes with climate-change mitigation. Greater rates and magnitude of climate change increase the likelihood of exceeding adaptation limits.

Final remarks

Observed climate change, attributable to anthropogenic greenhouse gas concentrations, have already affected multiple natural and human systems. Projections for the future indicate likelihood of emergence of key risks, spanning across systems, sectors and regions, that can be summarized in five integrative reasons for concern, illustrating what impacts can be regarded as “dangerous anthropogenic interference”, in the sense of the Article 2 of the UNFCCC. The essential reasons for concern refer to unique and threatened systems, extreme weather events, distribution of impacts, aggregated global impacts and large-scale singular events. Risks associated with those reasons for concern increase with warming.

References

Arent D.J., Döll P., Strzepek K.M., Jiménez Cisneros B.E., Reisinger A., Tóth F.L. and Oki, T. (2014) Cross-chapter box on the water–energy–food/feed/fiber nexus as linked to climate change. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Field C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 163—166.

Donatelli M., Srivastava A.K., Duveiller G. and Niemeyer S. (2012) Estimating Impact Assessment and Adaptation Strategies under Climate Change Scenarios for Crops at EU27 Scale. In: Environmental Modelling and Software. Seppelt R., A.A. Voinov, S. Lange and D. Bankamp (Eds.). Proceedings of Managing Resources of a Limited Planet, Sixth Biennial Meeting, July 2012, Leipzig, Germany, pp. 1—8.

IPCC (2013) Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

IPCC (2014) Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Field C.B., V.R. Barros, M.D. Mastrandrea, K.J. Mach (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Jiménez Cisneros B.E., Oki T., Arnell N.W., Benito G., Cogley J.G., Döll P., Jiang T. and Mwakalila S.S. (2014) Freshwater resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Field C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229—269.

Kovats R.S., Valentini R., Bouwer L.M., Georgopoulou E., Jacob D., Martin E., Rounsevell M. and Soussana J.-F. (2014) Europe. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Barros V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1267—1326.

Lung T., Lavalle C., Hiederer R., Dosio A. and Bouwer L.M. (2012) A multi-hazard regional level impact assessment for Europe combining indicators of climatic and non-climatic change. Global Environmental Change, 23(2), 522—536.

Porter J.R., Xie L., Challinor A.J., Cochrane K., Howden S.M., Iqbal M.M., Lobell D.B. and Travasso M.I. (2014) Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Field C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and L.L. White (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 485—533.

Schewe J., Heinke J., Gerten D., Haddeland I., Arnell N.W., Clark D.B., Dankers R., Eisner S., Fekete B., Colón-González F.J., Gosling S.N., Kim H., Liu X., Masaki Y., Portmann F.T., Satoh Y., Stacke T., Tang Q., Wada Y., Wisser D., Albrecht T., Frieler K., Piontek F., Warszawski L. and Kabat P. (2013) Multi-model assessment of water scarcity under climate change. Proceedings of the National Academy of Sciences of the United States of America. 111(9), 3247—3250.

Part II Observed climate change in Poland
4 Changes of air temperature in Poland

Dariusz Graczyk, Iwona Pińskwar, Adam Choryński, Małgorzata Szwed and Zbigniew W. Kundzewicz

4.1. Introduction

The authors of the IPCC Fifth Assessment Report (2014) estimate the global temperature rise by 0,85°C (± 0,2°C) in the period of 1880 to 2012. The global annual temperature has increased at an average rate of 0.07°C per decade in the last century, but in the last 50 years the average global air temperature has grown nearly 2 times faster, reaching a value of 0.13°C per decade (Trenberth et al., 2007). According to earlier predictions, the temperature is rising faster than the global average in higher latitudes, and slower near the equator. In Poland average annual temperature has increased between 1951 and 2000 at rate of 0.18°C per decade (Kożuchowski and Żmudzka, 2001). Based on more recent data (1951—2013) used in this study we can state that the rate of temperature increase in Poland is faster (more than 0.21°C per decade) as well. The year 2000 was the warmest in the period of 1951—2013, and four of five of the warmest years have occurred since 2000. Despite significant warming, the range of annual mean air temperature fluctuations is high even after 1990, and the difference between the warmest year 1990 (9.16°C) and the coldest year in the last decades of 1996 (6.24°C) was almost 3°C.

Higher winter temperatures cause decreased heating costs, and earlier start of the growing season and higher temperatures may also allow to grow plants with higher thermal requirements such as maize. These positive effects of increasing temperatures on some sectors are, often reduced or even eliminated, due to the an increasing number of extreme phenomena, such as heat waves witch are accompanied by droughts, what contributes to agricultural losses and increases the energy demand (e.g. air conditioning) during the hottest days.

4.2. Data and methods

The data used for the purpose of this research was provided by the the Institute of Meteorology and Water Management — State Research Institute. Time series were calculated based on air temperature records from 60 stations for daily maximum and minimum temperature, and 42 stations for daily mean temperature. The data usually cover the period between 1951 and 2013. Only five stations have slightly shorter records, all ending in 2013 and starting, between 1952 (Rzeszów) and 1958 Leszno).

The mean values for each examined climate indices have been calculated for two time intervals, periods of 1991—2013 and 1961—1990 — the recent standard reference period recommended by the World Meteorological Organization (WMO). Comparison of these two periods allowed to estimate changes after 1990 in the values of climate indices selected for analysis, important for different sectors of Poland.

4.3. Changes of air temperature in Poland in the period 1951—2013

Figure 4.1 shows the mean annual air temperature calculated for 42 meteorological stations in Poland. Despite very high natural variability over the years, in some cases even exceeding 3°C, we observe that the warmest years are very numerous at the end of the analysed period. Even 8 out of the 10 warmest years occurred after 1988. For comparison only 1 out of the 15 coldest years occurred after 1990. The coldest year in the study period was 1956 with an average annual air temperature 6.13°C, which was almost 3.5°C cooler than the record-breaking 2000 with temperature exceeding 9.5°C. After 1989, there were also only two years cooler than 7°C, which previously occurred much more frequently — 12 times in a 30-year period 1951—1980. After 1990 the average annual air temperature calculated for 42 stations was 0.75°C higher than in the period of 1961—1990. The difference between these periods was not the same in particular regions and varied between 0.5 and 1°C.

The rise in temperature was least felt in the south-east of Poland and in the Kłodzko Valley (0.5—0.6°C), and was mostly felt in the lane extending from the northern part of Lower Silesia through Great Poland to the central part of the Baltic coast, the Suwalki region and the Cracow region (0.8—0.9°C). In the southern part of Wielkopolska (Kalisz, Leszno) after 1990 have warmed by almost 1°C. Spatial distribution of air temperature anomalies is shown in Fig. 4.2.

Fig. 4.1. Average annual air temperature in years 1951—2013 calculated for data from 42 meteorological stations in Poland.

Fig. 4.2. Spatial distribution of positive anomalies of mean annual air temperature in the years 1991—2013, compared to 1961—1990 WMO standard reference period.

The difference in warming in Poland between 1961—1990 and 1991—2013 varied slightly between seasons. Mostly, the temperatures increased in summer on average by 1.17°C. Spring months, March-May, get warmer a little less, by about 0.9°C.

The mean temperature for winter increased by nearly the same value observed for the whole year and amounted to about 0.7°C. Across the country, warming was the least visible in the autumn months from September to November, and was only 0.21°C.

Similarly to the mean annual air temperature, seasonal anomalies for individual seasons were also varied, depending on the region of the country. The spatial distribution of these anomalies is shown in Figs 4.3—4.6.

In spring the northwest of Poland warmed the most (over 1°C). In the Lubuskie region the value of positive anomalies of air temperature exceeded 1.1°C. Significantly lower values (below 0.5°C) were recorded in the south-eastern part of the country. In the prevalent area, the value of positive anomaly ranged from 0.6 to 0.9°C, gradually increasing to the north-west.

During summer months warming was most noticeable in southern Poland, where in Małopolska and Dolny Śląsk temperature increased over 1.3°C and locally, near Wroclaw and Bielsko-Biała, over 1.5°C. The value of positive anomaly gradually decreased to the north, but for most area of Poland, still exceeded 1°C.

The lowest positive anomaly values of air temperature in autumn months were also accompanied by the smallest spatial variation comprised in the range of 0—0.4°C. Values above 0.3°C occurred only in a small area of southern Wielkopolska and Dolny Śląsk and in the coastal zone near Koszalin.

Winter months were characterised by distinct spatial diversity of the increase in air temperature. The lowest air temperature anomaly occurred in the southern Poland, where on a small area did not exceed 0.4°C. The value of anomalies increased gradually to the north, exceeding 1.1°C in the north-eastern part of the country.

Anomalies of average temperature calculated for the respective months (Table 4.1) are characterised by higher variability than for the seasons. While in November, 19 stations revealed even a small negative anomaly, for April and July (months, during which warming was the most visible) value of positive anomalies at many stations exceed 1.1°C.

Fig. 4.3. Spatial distribution of positive anomalies of air temperature during the spring months March — May in the years 1991—2013, compared to 1961—1990.

Fig. 4.4. Spatial distribution of positive anomalies of air temperature during the summer months June-August in the years 1991—2013, compared to 1961—1990.

Fig. 4.5. Spatial distribution of positive anomalies of air temperature during the autumn months September-November in the years 1991—2013, compared to 1961—1990.

Fig. 4.6. Spatial distribution of positive anomalies of air temperature during the winter months December-February in the years 1991—2013, compared to 1961—1990.

Table 4.1. Anomalies of mean monthly air temperatures in the period of 1991—2013, compared to 1961—1990.

4.4. Changes in the frequency of extremely high values of daily maximum air temperature

Figure 4.7a shows the mean number of hot days (daily maximum air temperature ≥ 30°C) during summer months (June to August), calculated for the years 1991—2013. The highest number of hot days (more than 10 days per year) were observed in the south and south-west of Poland, and the lowest, less than 2 days locally, in the south (mountains), and in the north (the Baltic coast). Almost all of the country in the 1990s experienced a clear increase in the frequency of hot days (Fig. 4.7b). In a large part of southern and western Poland, the number of hot days after 1990 doubled, and in some places even tripled compared to the 1961—1990 period.

(a) (b)

Fig. 4.7. (a) Average number of days with maximum air temperature ≥ 30°C during the summer months of 1991 to 2013. (b) The difference in “hot days” for summer between periods 1991—2013 and 1961—1990 (Graczyk et al., 2016).

Days with extremely high (over 35 degrees Celsius) maximum air temperature are currently very rare. They occur more than once a year, only locally, near the western border, and in the predominant area they are less frequent than once every 2 years (Figure 4.8a). In the north and east of Poland, high temperatures occur every few or even several years, mainly during the summer months, are considered to be extremely hot.

Compared to 1961—1991, the frequency of such temperatures has increased significantly. Almost in the entire south-western part of Poland, extremely hot days are now 3 times or even 4 times more frequent. The scale of that increase in the frequency of extremely hot days (Tmax ≥ 30°C) can be assessed by comparing the values of Figs 4.8a-b.

(a) (b)

Fig. 4.8. (a) Average number of days with a maximum air temperature ≥ 35°C during the summer months 1991 to 2013. (b) Difference in the number of “extremely hot days” in summer between periods of 1991—2013 and 1961—1990 (Graczyk et al., 2016).

(a) (b)

Fig. 4.9. (a) Average number of days with maximum air temperature ≥ 30°C occur during heat waves in the summer months in the period of 1991—2013. (b) Change in hot summer days in the period of 1991—2013 compared to 1961—1990 (Graczyk et al., 2016).

The increasing number of hot days in the year is also associated with an increase in their number during heat waves lasting 3 or more days. In some areas of southern Poland, their current number is on average 5 days a year, and over 60 % area of Poland exceeds 3 days in a year (Fig. 4.9a). Also in the case of this index, after 1990 there is a significant increase when comparing to the previously observed values (Fig. 4.9b). Almost on the entire Polish territory hot days during heat waves occur now at least 50 % more frequently, and in the south and south-east of the country, their number has even doubled.

4.5. Changes in the frequency of extremely low values of daily minimum air temperature

Days with minimum air temperature ≤ -10°C (very cold) now occur most often in mountain areas, where their number can average up to 70 per year (for the highest elevated station Kasprowy Wierch). In the rest of the country their number does not exceed 25 days a year, reaching close to that number only in the east of the country. The number of very cold days is gradually decreasing in the west direction, in western Poland and the Baltic coast is in the range of 5—10 days (Fig. 4.10a). In the years 1961—1990 days with Tmin ≤-10°C were in Poland significantly more numerous than after 1990. The decrease in their number in recent decades, expressed in days, was the highest in areas where they occur most frequently, in the mountains and in the eastern part of the country where it reached 8 days a year. The percentage decrease in the number of very cold days in the western and north-western Poland, was for many meteorological stations higher than in the east and exceeded 30 %.

(a) (b)

Fig. 4.10. (a) Average annual number of days with minimum air temperature ≤ -10°C for years 1991—2013. (b) Change in annual number of very cold days in 1991- 2013 compared to the period of 1961—1990.

Days with minimum air temperature ≤ -15°C (extremely cold) occur in Poland several times less than the very cold days (Tmin ≤ -10°C). Locally, on the Baltic coast, their frequency is less than 1 day a year. Their number is increasing similarly to very frosty days towards the east, exceeding an average of 10 days a year near the eastern border (Fig. 4.11a). Compared to the years 1961—1990, the highest decrease in the number of extremely cold days, more than 4 days per year, occurred not only in the eastern part of the country, but also in the north-western part of Poland (Figure 4.11b). On a small area of southern Poland near Opole and Racibórz, the number of extremely cold days after 1990 has practically remained unchanged.

(a) (b)

Fig. 4.11. (a) Average annual number of days with minimum air temperature ≤ -15°C for years 1991—2013. (b) Change in annual number of very cold days for years 1991- 2013 compared to the period of 1961—1990.

Figure 4.12a shows the spatial distribution of length (expressed in days) of the longest period in a year, in which the minimum daily air temperature is less than or equal to -10°C. Periods lasting more than 5 days, where every day the minimum daily air temperature is less than or equal to -10°C, after 1990, occurred in the eastern part of the country, exceeding 9 days in the north-east edge. Similarly long cold waves were also observed in the Tatra Mountains. Compared to the years of 1961—1990, the longest period of very cold days shortened by more than 2 days, only on a small area of the eastern and central Poland. In other regions, there was no noticeable shortening of this period, locally in southern Poland, it was even slightly (<1 day) longer (Fig. 4.12b).

(a) (b)

Fig. 4.12. (a) The longest period in a year with daily minimum air temperature ≤ -10°C for the years 1991—2013. (b) Change in duration of the longest period in a year with daily minimum air temperature ≤ -10°C for the period of 1991—2013 compared to 1961—1990.

(a) (b)

Fig. 4.13. (a) The longest period in a year with daily minimum air temperature ≤ -15°C for the years 1991—2013. (b) Change in duration of the longest period in a year with daily minimum air temperature ≤ -15°C for the period of 1991—2013 compared to 1961—1990.

Cold waves, during which the minimum temperature for the consecutive days is lower than or equal to -15°C in the western part Poland, and especially at the Baltic Sea, occur only during more severe winters, and therefore the value of the annual mean of these thermal characteristics does not exceed 3 days there, and in the coastal area even one day. Only in the east of Poland and in the Tatra Mountains, where the average winters are sharply most severe, almost every year (except very mild winters), we can observe waves of extremely cold days, lasting 4 or even 7 days (Fig. 4.13a). Compared to the winters of 1961—1990 (Figure 4.13b), waves of extremely low temperatures shortened the most in the Tatra Mountains, and in the north-western part of the Poland, but this change has not exceeded on average one day per year anywhere. For the rest of country the length of extreme cold periods minimally shortened (0.2 days per year), or even slightly increased (up to about 0.6 days a year).

4.6. Summary

Over last decades, the average annual air temperature has increased in entire Poland. The value of this growth is not the same for different regions and ranges from 0.5 to 1.0°C, usually exceeding 0.7°C. Climate of Poland is characterised with high variability of air temperatures over years, it causes that, despite the progressing warming, there are still years much cooler than the average, although clearly less frequent than before 1991. Taking into account the climatic seasons, more warmer are summer and spring, mainly due to a large increase of average temperatures in July (1.52°C) and April (1.23°C). The lowest temperature increase was noted for the climatic autumn, which is only slightly warmer (<0.3°C) than the average for autumn in years 1961—1990. During two autumn months, September and November, at some stations even a slight decrease of average monthly air temperature occurred. Similar tendency was also observed in other European countries, e.g. Austria (Nemec et al., 2013). It was explained by changes in the frequency of some types of circulation in autumn months.

Warming in summer months was reflected by a very clear increase of the frequency of extremely high temperatures. The number of hot days (Tmax ≥ 30°C) in southern Poland has even doubled in relation to earlier years. Proportionally, even more visible has been the increase in the number of extremely hot days (Tmax ≥ 35°C), which take place currently up to 4 times more frequently than before 1991. Fortunately this threshold of extremely high temperatures was not exceeded every year, but during the most intense heat waves, even more than 3—5 days with maximum temperature higher than 35°C were recorded.

Average winters also are becoming warmer, which translates into a reduction in the number of very cold days (Tmin ≤ -10°C) and extremely cold days (Tmin ≤ -15°C). However, there still occur the winter months, during which we observe long and intense cold waves. In the case of frost waves, when daily minimum temperature in consecutive days falls continuously below -15°C, we cannot conclude that there is an observed trend in the overwhelming area of the country, that could indicate shortening of such episodes.

References

Graczyk D., Pińskwar I., Kundzewicz, Z.W., Hov Ø., Forland E.J., Szwed M. and Choryński A. (2016). The heat goes on-changes in indices of hot extremes in Poland. Theor. Appl. Climatol., DOI:10.1007/s00704—016-1786-x.

IPCC (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri R.K. and Meyer L.A. (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

Kożuchowski K. and Żmudzka E. (2001) Ocieplenie w Polsce: skala i rozkład sezonowy zmian temperatury w drugiej połowie XX w. Prz. Geof., 46(1—2), 81—90.

Nemec J., Gruber C., Chimani B. and Auer I. (2013) Trends in extreme temperature indices in Austria based on a new homogenized dataset. Int. J. Climatol., 33, 1538—1550.

Trenberth K.E., Jones P.D., Ambenje P., Bojariu R., Easterling D., Klein Tank A., Parker D., Rahimzadeh F., Renwick J.A., Rusticucci M., Soden B. and Zhai P. (2007) Observations: Surface and Atmospheric Climate Change. W: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. (Solomon S., Qin D., Manning M., Chen Z., Marquis M., Averyt K.B., Tignor M., Miller H.L. (red.)), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

5 Changes in precipitation in Poland

Iwona Pińskwar, Adam Choryński, Dariusz Graczyk,

Małgorzata Szwed and Zbigniew W. Kundzewicz
5.1. Introduction

Annual mean precipitation total for Poland especially for lowlands and highlands, which are covering most of the country, ranges from above 500 to 700 mm. Higher values occur in the southern part of Poland, in the mountainous area. Freshwater resources in Poland calculated per inhabitant are among the smallest in Europe (GUS, 2016), therefore every change in precipitation has an impact on different sectors of human activity in the country, including agriculture, energy production and industry. Changes in mean precipitation and their distribution in particular months are especially important for agriculture and ecosystems, while changes in extreme precipitation may affect risk of inundations and urban floods or droughts resulting from low sum of precipitation, particularly if it is accompanied with high air temperatures. This paper analyses changes in annual, seasonal and monthly precipitation totals and looks at the issue of changes in extreme precipitation.

5.2. Data and methods

Precipitation datasets used in this study have been provided by the Institute of Meteorology and Water Management (IMGW-PIB). Table 5.1 shows a list of 46 stations and mean annual precipitation for periods 1961—1990 and 1991—2015, as well as minimum and maximum annual precipitation with year of the record occurrence for whole available period. Precipitation data for most of these stations cover the period 1951—2015, only for a few stations the initial year of data collection was different: Rzeszów: 1952; Nowy Sącz: 1954; Terespol and Lesko: 1955; Leszno: 1958; Mława: 1961.

The highest number of annual minimum precipitation records in a single year from this dataset occurred during droughts: in 1982 (9 stations), then in 1959 (8 stations), in 1951 and 2015 (5 stations each). Year 2015 with severe drought brought a new record for the lowest annual precipitation: 259 mm in Kalisz, merely 51 % of mean annual precipitation for 1961—90. By this time the lowest annual precipitation was recorded in Poznań: 275 mm in 1982 (http://www.imgw.pl/index.php?option=com_content&view= article&id=251&Itemid=285).

Most of maximum annual precipitation records occurred in 2010 (the year with large flood — records occurred at 9 stations), then in 1970 (8 stations) and in 1966 (6 stations). The highest annual precipitation sum in this dataset was observed in mountainous station Kasprowy Wierch (1991 m a.s.l.): 2600 mm in 2001 and for station lower situated: Zakopane (857 m a.s.l) 1645 mm in 2010.

Figure 5.1 shows spatial coverage of precipitation data for Poland which covers in a nearly-uniform way the whole country.

Fig. 5.1. Location of stations with precipitation data used in this study.

Based on data, the following indices related to precipitation were calculated:

annual precipitation;

number of wet days with daily precipitation ≥ 1 mm;

Simple Daily Intensity Index SDII — the ratio of annual total precipitation to the number of days during the year with daily precipitation equal to or greater than 1 mm;

relation of sum of precipitation in the warm season (Apr. — Sept.) to precipitation in the preceding cold season (Oct. — March);

precipitation total for the cold half-year (Oct. — March) and the warm half-year (Apr. — Sept.);

precipitation sums in particular seasons (winter: Dec. — Feb. (DJF); spring: March — May (MAM); summer: June — Aug. (JJA) and autumn: Sept. — Nov. (SON)) and individual months;

maximum seasonal 24h precipitation for the cold season (Oct. — March) and the warm season (Apr. — Sept.);

number of days with intense precipitation ≥ 10 mm per day;

number of days with very intense precipitation ≥ 20 mm per day;

maximum monthly precipitation;

seasonal (Apr. — Sept.) maximum number of consecutive dry days CDD — longest period with daily precipitation below 1 mm.

In this research, in order to show changes in precipitation for every index, mean values for 1991—2015 and the WMO climate standard normal period 1961—1990 have been compared. Changes are calculated as the percentage change.

5.3. Results

5.3.1. Changes in precipitation totals for yearly, half-yearly, seasonal and monthly resolution

The analysis of annual precipitation shows an increase for 32 out of 46 stations: for 23 stations it was lower than +5 % and for 9 stations it was higher than +5 %. It can be noticed that northern, central and south-eastern parts of Poland are more humid. For other 14 stations, annual precipitation for the interval 1991—2015 is lower than for the earlier interval of 1961—90. The largest decrease is detected for Śnieżka (˗13 %; see Fig. 5.2).

For the number of wet days with precipitation equal to or greater than 1 mm decrease is noted at more than half of 46 stations (at 6 more than ˗5 % and at 21 below ˗5 %). More wet days are observed for 19 stations, wherein only for 2 stations these changes are larger than +5 %.

Table 5.2 presents mean values of the number of wet days with precipitation equal to or greater than 1 mm, SDII, ratio of precipitation total in the warm season (Apr. — Sept.) to precipitation total in the preceding cold season (Oct. — March) and precipitation total for particular months and seasons for the interval 1961—1990. It allows to assess how large is the relative change, in percent.

Fig. 5.2. Percentage change of mean annual precipitation (left) and the mean number of wet days with precipitation equal to or greater than 1 mm (right) — mean for the interval 1991—2015 related to the mean for 1961—1990.

Figure 5.3 shows percentage change of Simple Daily Intensity Index SDII — the ratio of annual total rainfall to the number of days during the year with precipitation equal to or greater than 1 mm (right) and percentage change of the relation of precipitation total in the warm season (Apr. — Sept.) to precipitation total in the preceding cold season (Oct. — March). The value of SDII depends on annual precipitation amount and annual numbers of wet days, therefore, changes of these indices affect changes of SDII. The SDII demonstrates an increase for the last 25 years (1991—2015) related to the interval 1961—90 for a large area of Poland: for 40 out of 46 stations. It was lower than +5 % for 33 stations and larger than +5 % for 7 stations. Only for 1 station (Śnieżka) the decrease exceeds ˗9 %, while for other 5 stations these decreases are below ˗5 %.

One can draw a conclusion that increase in the annual precipitation total is due to an increase of the amount of precipitation during wet days. This can be stated with confidence about such stations as: Kołobrzeg, Łeba, Suwałki, Toruń, Mława, Białystok, Zielona Góra, Leszno, Włodawa, Katowice, Kielce, Tarnów and Zakopane. For these 13 stations the mean annual precipitation is higher for the last 25 years (1991—2015) and the mean number of wet days (with daily precipitation equal to or greater than 1 mm) is lower than the previous mean calculated for 1961—90.

The ratio of precipitation total in the warm season (Apr. — Sept.) to precipitation total in the preceding cold season (Oct. — March) shows a decreasing trend for 26 stations and decrease of this index is larger than increase for 20 other stations: small decrease of 5 % is noticed for 15 stations, of 5 — 10 % for 5, and two larger decreases: 18 % for Elbląg and 27 % for Śnieżka. Small increase (below +5 %) of the ratio of precipitation is observed for 12 stations, a 5 — 10 % increase for 5, and a 10 — 15 % for 3 stations.

Fig. 5.3. Percentage change of Simple Daily Intensity Index SDII (left) and the relation of sum of precipitation in the warm season (Apr. — Sept.) to precipitation in the preceding cold season (Oct. — March) (right) — mean for the interval 1991—2015 related to the mean for 1961—1990.

Changes in the ratio of precipitation totals in the warm season (Apr. — Sept.) and the preceding cold season (Oct. — March) depends on changes of precipitation totals for these two parts of a year. Decrease in the ratio of precipitation may be due to an increase of precipitation total for the cold half-year or a decrease of precipitation total for the warm half-year or when increase of precipitation total for the warm half-year is smaller than increase of precipitation total for the cold half-year. Figure 5.4 shows precipitation totals for these two parts of the year (Oct. — March and Apr. — Sept.).

Sum of precipitation for the cold half-year increases at 29 stations: below +5 % at 17, from +5 to +10 % at 10, and 2 above +10 % (+13 % at Chojnice and +17 % at Elbląg). Decreases of this index are visible at 17 stations: below ˗5 % at 8, from ˗5 to ˗10 % at 6, and about ˗10 % at 3 stations (Ustka, Opole and Kasprowy Wierch). Sum of precipitation for the warm half-year increases at 27 stations (less than during the cold half-year): below 5 % at 22, from +5 to +10 % at 3, and 2 above +10 % (+10 % at Szczecin and +16 % at Świnoujście) and decreases at 19 stations: below ˗5 % at 14, from ˗5 to ˗10 % at 4, and one larger decrease at Śnieżka (˗26 %). Despite of increase of precipitation for the warm half-year, the ratio of precipitation decreases for 14 stations: Koszalin, Łeba, Hel, Chojnice, Poznań, Koło, Terespol, Zielona Góra, Jelenia Góra, Tarnów, Rzeszów, Zakopane and Lesko.

Fig. 5.4. Percentage change of precipitation total for the cold half-year (Oct. — March) (left) and precipitation total for the warm half-year (Apr. — Sept.) (right) — mean for the interval 1991—2015 related to the mean for 1961—1990.

Looking at the seasonal sums of precipitation (Fig. 5.5) these findings are confirmed. For central and northern part of Poland, winter (DJF) is getting wetter for the period 1991—2015 related to the period 1961—90. Similarly, an increase in precipitation total is visible for spring (MAM). For this season, decreases in precipitation sums are noticeable mainly in the south-western part of Poland. In the case of precipitation in summer (JJA) and autumn (SON) the changes in precipitation sums are lower with more negative changes.

The analyses of changes in precipitation total in particular months (Fig. 5.6) enable to answer the question: which months are responsible for changes in precipitation total for the seasons. Precipitation total for December is decreasing for nearly all stations located in the central and southern part of Poland. The change is positive only for 11 stations, among others for Śnieżka (+14 %) and Chojnice (+20 %).

In contrast precipitation total for January increases for nearly all stations. Decreases are only present at 7 stations with the largest change (˗13 %) for Ustka and Kasprowy Wierch.

Also for February the mean precipitation total for last 25 years (1991—2015) is larger than the earlier interval 1961—90 for stations situated in the northern and central part of Poland as well as in the southern part except for Kasprowy Wierch (18 % decrease). Increase in the winter (DJF) precipitation total is due to an increase of January and February precipitation sums. However, for 4 stations: Lublin, Tarnów, Rzeszów and Zakopane, despite of increase in precipitation total for January and February, the precipitation total for winter is decreasing. For another 6 stations, such as: Włodawa, Opole, Racibórz, Kielce, Sandomierz and Kasprowy Wierch, precipitation total is decreasing for all winter months (Figs. 5 and 6).

Fig. 5.5. Percentage change of precipitation total for the seasons: winter (DJF) (top left), spring (MAM) (top right), summer (JJA) (bottom left) and autumn (SON) (bottom right) — mean for interval the 1991—2015 related to the mean for 1961—1990.

March is the month with the largest positive changes in precipitation total: the highest of them is in Poznań (+50 %), for 5 stations (Chojnice, Koło, Zielona Góra, Legnica and Jelenia Góra) the change is above +40 % and only for 2 stations (Ustka and Racibórz) the change is negative. The first month of the vegetation season, April, is getting drier in the western part of Poland and a bit more humid in the eastern part of the country. In May, one can observe positive changes in precipitation total over a large area of the country, except for the south-western part of Poland. Despite of wetter March, spring is getting drier in the south-western part of Poland (Figs. 5.5 and 5.6).

Fig. 5.6. Percentage change of mean precipitation total for particular calendar months — mean for the interval 1991—2015 related to the mean for 1961—1990.

Total precipitation in June decreases on a large researched area. Only at 6 stations located in the north of the country (Szczecin, Świnoujście, Kołobrzeg, Koszalin, Ustka and Łeba) and 4 in the south-eastern Poland (Nowy, Sącz, Rzeszów, Kielce and Włodawa) increase in precipitation total is noted. July is getting wetter, especially in the west. The largest positive changes are visible at Słubice (+54 %), Świnoujście (+43 %), Zielona Góra (+37 %) and Legnica (+31 %). The negative changes in precipitation total for this month can be observed in the north (the largest above ˗10 % at: Kołobrzeg, Ustka and Łeba stations).

In turn, precipitation total for August decreases in the central and southern Poland (above ˗30 % in Opole and above ˗20 % at 7 stations: Bielsko-Biała, Śnieżka, Tarnów, Łódź, Wieluń, Lublin and Zakopane). Precipitation increases mainly at north-western stations: Kołobrzeg (+32 %), Koszalin and Chojnice (above +20 %), Ustka, Szczecin, Łeba and Siedlce on the east (above +15 %). Changes in precipitation sums for these three months result in changes for summer precipitation total: decrease in southern and central parts and increase in western and north-western parts of the country (Figs. 5.5 and 5.6).

Precipitation sum for September increases more than other autumn months. The largest increase is in the southern and central part of Poland: above +30 % in Rzeszów, Sandomierz and Tarnów, above +20 % in Bielsko-Biała, Katowice, Kłodzko, Lublin and Toruń. Small decrease is noticed in the north. In contrast, October is getting drier for the whole central Poland and wetter in the northern (the most in Elbląg, +26 %) and south-eastern parts of the country (the most in Lesko, +23 %), while precipitation total for November decreases for nearly whole area of the research (the most in: Ustka, Mława, Wrocław, Kalisz, and Wieluń — all decreases exceeded ˗20 %), with 2 increases in: Katowice and Lesko. As a result precipitation sum for autumn decreases in the central and northern parts and increases in the south-eastern part of Poland (Figs. 5.5 and 5.6).

5.3.2. Changes in extreme precipitation

Table 5.3 shows mean values of extreme precipitation indices for the interval 1961—1990 and the maximum value of maximum daily and monthly precipitation sums for the whole research period, with their date of observation. Most of maximum daily precipitation records occurred in 2011: 5 records, then in 2001: 4 records and in 2010: 3 records (both years, 2001 and 2010 with floods). The largest value in this dataset occurred at a mountainous station, Kasprowy Wierch (232 mm in 30 June 1973), while at a lower located station — 162.7 mm in Bielsko-Biała (16 May 2010). Maximum monthly precipitation sums in research period occurred most often in July 1997 (month with dramatic flood) and July 2011 (6 records each), and then 5 records in July 2001 (flood month) and in August 2006 (very wet August after very dry July). The highest value of maximum monthly precipitation sum in this dataset was observed in the flood month — July 2001 at Kasprowy Wierch (654.1 mm) and 511.5 mm at lower situated station Bielsko-Biała (in May 2010; also flood month).

Figure 5.7 presents percentage change of daily maximum precipitation for the cold season (Oct. — March) and the warm season (Apr. — Sept.) for the period 1991—2015, related to 1961—1990. Maximum daily precipitation for cold season decreases in the west of Poland and increases in the south (except for: Kasprowy Wierch and Zakopane, with negative changes) and north, with highest increases above +20 % in Racibórz, Mława and Elbląg.

Decreases of the seasonal 24h precipitation for the warm period are lower than for the cold period (the largest decrease is for Śnieżka ˗28 %). Daily maximum precipitation increases the most for the stations: Świnoujście (+28 %), Racibórz (+23 %), Włodawa (+22 %), Poznań (+19 %) and Jelenia Góra (+17 %) (Fig. 5.7).

Fig. 5.7. Percentage change of daily maximum precipitation for the cold season (Oct. — March) (left) and the warm season (Apr. — Sept.) (right) — mean for the 1991—2015 interval related to the mean for 1961—1990.

Changes in the number of days with intense precipitation (equal to or greater than 10 mm) and in the number of days with very intense precipitation (equal to or greater than 20 mm) are presented in Figure 5.8.

Number of days with daily precipitation equal to or greater than 10 mm increases especially in the north-western part of Poland, then a bit less in the central and south-eastern parts of the country.

Changes in the number of days with very intense precipitation (equal to or greater than 20 mm) have a similar spatial distribution, but are larger. Increases are especially visible for Świnoujście (+116 %), Szczecin (+70 %), Chojnice (+34 %), Białystok (+24 %) and Włodawa (+24 %) (Fig. 5.8).

Fig. 5.8. Percentage change of the number of days with intense precipitation (equal to or greater than 10 mm) (left) and of the number of days with very intense precipitation (equal to or greater than 20 mm) (right) — mean for the 1991—2015 interval related to the mean for 1961—1990.

Figure 5.9 shows the last two of the analysed indices of extreme precipitation, that is: maximum monthly precipitation sum and seasonal maximum number of consecutive dry days CDD (the longest dry period with daily precipitation below 1 mm) during the warm season from April to September. Maximum monthly precipitation sum decreases only for 10 stations and changes are small, the largest one for Śnieżka (˗9 %), for other stations one can observe increases, with the largest in: Włodawa (+23 %), Słubice (+22 %), Lublin (+21 %), Świnoujście, Lesko, Tarnów, Szczecin and Siedlce (all changes above +15 %).

Fig. 5.9. Percentage change of maximum monthly precipitation total (left) and number of consecutive dry days, CDD, (the longest dry period with daily precipitation below 1 mm) during the warm season from April to September (right) — mean for the 1991—2015 interval related to the mean for 1961—1990.

One drought index — maximum dry period with daily precipitation below 1 mm during the warm season from April to September, is getting longer (orange colour for positive changes and purple for negative changes in Figure 5.9). This increase is visible for 32 stations out of 46, the most for: Chojnice (+23 %), Łeba (+22 %) and Lublin (+17 %). For Kłodzko consecutive dry days CDD decrease strongest ˗15 %.

5.5. Conclusions

The annual precipitation increases in the northern, central and south-eastern parts of Poland, while the south-western part of the country is getting drier. Intensity of precipitation during wet days (with precipitation equal to or greater than 1 mm) increases as shown in analyses of the number of wet days with precipitation equal to or greater than 1 mm and SDII.

For many stations, decreases of the ratio of the sum of precipitation in the warm season (Apr. — Sept.) to the sum of precipitation in the preceding cold season (Oct. — March) were observed. These decreases are mainly due to increases of precipitation sums during the cold half of year (Oct. — March). The ratio of precipitation in these two parts of year (Oct. — March and Apr. — Sept.) decreases even at these stations, where precipitation sum for the warm half-year is increasing.

The changes reflected in the ratio of the sums of precipitation are due to shifts and changes in precipitation total in particular seasons and months. For central and northern parts of Poland, winter (DJF) has got wetter in the last 25 years (1991—2015). This increase is mainly due to increases in precipitation total during January and February, because precipitation total for December decreases for nearly all stations located in the central and southern parts of Poland.

Also precipitation sum for spring (MAM) increases; except for south-western part of Poland. March is getting wetter at nearly all stations. April, in turn, is getting drier in the western part of Poland. Precipitation during May increases, except for the south-western part of the country.

The changes in summer (JJA) and autumn (SON) precipitation sums are smaller with decreases prevailing. Summer precipitation totals decrease in southern and central parts and increase in the western and north-western parts of the country. Total precipitation for June decreases in a large area, while July is getting wetter, especially in the west and precipitation totals for August decrease in the centre and the south of Poland.

Precipitation sums for autumn decrease in the centre and north and increase in the south-east of Poland. These changes result from increases in precipitation sum for September and decreases in precipitation total for the whole central Poland during October and decreases for November for nearly the whole researched area.

These findings are consistent with trends in annual and summer precipitation across Europe between 1960 and 2015 (EEA, 2017). For gridded data, there is an increasing trend (0 — 20 mm/decade) in the northern and eastern parts of Poland for annual precipitation and a decreasing trend for summer precipitation in the south-eastern (above ˗15 mm/decade) and central parts (0 — ˗10 mm/decade) of Poland and an increasing trend for summer precipitation in northern part of the country (0 — 5 mm/decade).