Climate change and its impact
on selected sectors in Poland
Editors: Zbigniew W. Kundzewicz
Technical editing: Iwona Pińskwar and Małgorzata Szwed
Cover design: Adam Choryński
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; email@example.com
Andrzej Ceglarz, Potsdam Institute for Climate Impact Research, Potsdam, Germany; firstname.lastname@example.org
Adam Choryński, Institute of Agricultural and Forest Environment of the Polish Academy of Sciences, Poznań, Poland; email@example.com
Andreas Dobler, Norwegian Meteorological Institute, Oslo, Norway; firstname.lastname@example.org
Eirik Johan Førland, Norwegian Meteorological Institute, Oslo, Norway; email@example.com
Dariusz Graczyk, Institute of Agricultural and Forest Environment of the Polish Academy of Sciences, Poznań, Poland; firstname.lastname@example.org
Jan Erik Haugen, Norwegian Meteorological Institute, Oslo, Norway; email@example.com
Øystein Hov, Norwegian Meteorological Institute, Oslo, Norway; firstname.lastname@example.org
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; email@example.com
Valentina Krysanova, Potsdam Institute for Climate Impact Research, Potsdam, Germany; firstname.lastname@example.org
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; email@example.com
Paweł Marcinkowski, Warsaw University of Life Sciences (SGGW), Warsaw, Poland; P.Marcinkowski@levis.sggw.pl
Abdelkader Mezghani, Norwegian Meteorological Institute, Oslo, Norway; firstname.lastname@example.org
Anna Nieróbca, Institute of Soil Science and Plant Cultivation (IUNG) — State Research Institute, Puławy, Poland; email@example.com
Joanna O’Keefe, Warsaw University of Life Sciences (SGGW), Wasaw, Poland; firstname.lastname@example.org
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; email@example.com
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; firstname.lastname@example.org
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; email@example.com
Marta Utratna, Warsaw University of Life Sciences (SGGW), Warsaw, Poland; M.Utratna@levis.sggw.pl
Table of contents
Part I Setting the stage
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.
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 mmyr–1 between 1901 and 2010. The SLR clearly advanced more recently, reaching 2.0 mmyr–1 between 1971 and 2010 and 3.2 mmyr–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 mmyr–1), changes in glaciers (0.76 mmyr–1), Greenland ice sheet (0.33 mmyr–1), Antarctic ice sheet (0.27 mmyr–1), and land water storage (0.38 mmyr–1). The sum of these contributions slightly exceeds 2.8 mmyr–1 (in comparison to the estimate of 3.2 mmyr–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 Wm−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 Wm−2, while emissions of CO2 alone and of CH4 alone have caused an RF of 1.68 Wm−2 and 0.97 Wm−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 Wm−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