Impacts of climate change on global agriculture accounting for adaptation

<p>Under continued high anthropogenic CO<sub>2</sub> emissions, the atmospheric CO<sub>2</sub> concentration around 2100 will be like that of the Early Eocene Climate Optimum (EECO, 56–48 Ma) hothouse period. Hence, reconstructions of the EECO climate give insight into the workings of the climate system under the possible future CO<sub>2</sub> conditions. Our current understanding of global mean surface temperature (GMST) during the Cenozoic era relies on paleo-proxy estimates of deep-sea temperature (DST) combined with assumed relationships between global mean DST (GMDST), global mean sea-surface temperature (GMSST), and GMST. The validity of these assumptions is essential in our understanding of past and future climate states under hothouse conditions.<br>We analyse the relationship between these global temperature indicators for the end-of-simulation global mean temperature values in 25 different millennia-long model simulations of the EECO climate under varying CO<sub>2</sub> levels, performed as part of the Deep-Time Model Intercomparison Project (DeepMIP). The model simulations show limited spatial variability in DST, indicating that local DST estimates can be regarded representative of GMDST. Linear regression analysis indicates that GMDST and GMST respond stronger to changes in atmospheric CO<sub>2</sub> than GMSST by factors 1.18 and 1.17, respectively. Consequently, the responses of GMDST and GMST to atmospheric CO<sub>2</sub> changes are similar in magnitude. This model-based analysis indicates that changes in GMDST can be used to estimate changes in GMST during the EECO, validating the assumed relationships. To test the robustness of these results, other Cenozoic climate states besides EECO should be analysed similarly.</p>

Changes in extreme weather events are a potentially important aspect of anthropogenic climate change (ACC), yet they are difficult to attribute to ACC because the record length is often similar to, or shorter than, extreme-event return periods. This study is motivated by the “World Weather Attribution” (WWA) initiative and, specifically, their approach of fitting extreme value distribution functions to local observations. They calculate the dependence of distribution parameters on global mean surface temperature (GMST) and use this dependence to attribute extreme events to ACC. Applying this method to preindustrial climate simulations with no time-varying greenhouse gas forcing, we still find a strong dependence of distribution parameters on GMST. This dependence results from internal climate variability (e.g., ENSO) affecting both extreme events and GMST. Therefore, dependence on GMST does not necessarily imply an effect of ACC on extremes. We further consider whether an extreme value, normal, or lognormal distribution better represents the data, if a GMST dependence of distribution parameters is justified using a likelihood ratio test, and if a meaningful attribution is possible given uncertainties in GMST dependence. We find, for example, that an attribution of Australia’s 2020–21 bushfires to ACC is difficult due to the effects of internal variability. For the 2019–21 drought in Madagascar, we find that the small number of available data points precludes a meaningful attribution analysis. Overall, we find that the effects of internal climate variability on GMST and the uncertain relationship between GMST and regional extremes may lead to inaccurate attribution conclusions using the part of the WWA approach examined here.

Interannual global mean surface temperature (GMST) forecast provides critical insights into the economic and societal implications of climate variability. The pronounced GMST elevation in 2023–2024 indicates that the Earth may have accumulated enough heat to cause widespread disasters, underscoring the necessity for establishing accurate short-term GMST predictions to offer timely and sustainable public service. However, capturing high-frequency annual variability (ANV) component of GMST poses challenges due to its susceptibility to intraseasonal-to-interannual (ISI) noises, particularly across the Northern Hemisphere’s mid-to-high latitudes. Averaging these ISI variations in November and December effectively enhances signal clarity, especially over oceans, and masks unpredictable noises on land. By forecasting the average GMST for November and December to extract ANV predictability, a strategy for annual GMST prediction was established. This approach successfully advanced precise GMST hindcasts by up to 2-months during 1980–2022, exceeding performance of existing climate models and boosting early warning for interannual GMST shifts.

Quantitatively projecting the impact of future climate change on the socio-economy and exploring its internal mechanism are of great practical significance to adapt to climate change and prevent climate risks. Based on the economy-climate (C-D-C) model, this paper introduces a yield impact of climate change (YICC) model that can quantitatively project the climate change impact. The model is based on the YICC as its core concept and uses the impact ratio of climate change (IRCC) indicator to assess the response of the economic system to climate change over a long period of time. The YICC is defined as the difference between the economic output under changing climate condition and that under assumed invariant climate condition. The IRCC not only reflects the sensitivity of economic output to climate change but also reveals the mechanism of the nonlinear interaction between climate change and non-climatic factors on the socio-economic system. Using the main grain-producing areas in China as a case study, we use the data of the ensemble average of 5 GCMs in CMIP6 to project the possible impact of climate change on grain production in the next 15–30 years under three future scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5). The results indicate that the long-term climate change in the future will have a restraining effect on production in North region and enhance production in South region. From 2021 to 2035, climate change will reduce production by 0.60–2.09% in North region, and increase production by 1.80–9.01% in South region under three future scenarios. From 2021 to 2050, compared with the climate change impact in 2021–2035, the negative impact of climate change on production in North region will weaken, and the positive impact on production in South region will enhance with the increase in emission concentration. Among them, climate change will reduce grain output in North region by 0.52–1.99%, and increase output in South region by 1.35–9.56% under the three future scenarios. The combination of economic results and climate change research is expected to provide scientific support for further revealing the economic mechanism of climate change impacts.

Climate feedbacks are sensitive to the geographical distribution of sea surface temperature (SST). This sensitivity, called the pattern effect, affects the amplitude of the Earth radiative response to anomalies in global mean surface temperature (GMST) and thus is essential in shaping the global energy budget dynamics. Zero-dimensional energy balance models (EBMs) are the simplest representation of the global energy budget dynamics. Many only depend on GMST anomalies and cannot account for the pattern effect explicitly. In EBMs, the pattern effect leads to apparent variations of the global climate feedback parameter λ. Assuming a variable λ in EBMs enables them to more accurately reproduce AOGCM simulations of the GMST anomalies but it leads to variations in λ of >+15%. These large variations mean λ is not a constant and the Taylor expansion underpinning EBMs’ formulation does not hold, casting doubts on the physical grounding of such EBMs. Here we propose a new EBM based on a multivariate linear Earth radiative response, which depends on both the GMST and the surface warming pattern. The resulting multilinear EBM accurately reproduces AOGCM simulations of anomalies in Earth radiative response and GMST under abrupt 4xCO2 forcing. When interpreted in terms of variable λ, the multivariate EBM leads to small variations in λ that are physically consistent with the underpinning Taylor expansion. We analyze with the multivariate framework the variations of the planetary heat uptake N as a function of the GMST and the pattern of warming through a 3D generalization of the Gregory plot. We show that the apparent nonlinear behavior of the radiative response of the Earth against GMST seen in classical monovariate EBMs (and in classical Gregory plots) can actually be explained by a bilinear dependance of the radiative response of the Earth on the GMST and the pattern of warming. The multivariate EBM further provides an explicit dependence of the global energy budget on the pattern of warming and on the climate state. It has important consequences on the expression of the climate sensitivity.

The accurate quantification of uncertainties in regional sea‐level projections is essential for guiding policy makers. As climate models do not currently simulate total sea level, these uncertainties must be quantified through summation of uncertainties in individual sea‐level components. This summation depends on the correlation between the components, which has previously been prescribed or derived from each individual component's dependence on global mean surface temperature. In this study, we quantify, for the first time, regional correlations between sea‐level components based on regional climate change projections. We compute regional sea‐level projections consistent with climate projections from an ensemble of 14 Earth System Models. From the multi‐model spread, we estimate the uncertainty in the regional climate's response to greenhouse forcing. To quantify the total uncertainty, we add the uncertainty in the response of sea‐level components to this regional climate change. This approach reveals how regional climate processes impose correlations between sea‐level components, affecting the total uncertainty. One example is an anti‐correlation between North Atlantic sterodynamic change and Antarctic dynamic mass loss, suggesting a teleconnection established by the large‐scale ocean circulation. We find that prescribed correlations, applied in the fifth assessment report of the Intergovernmental Panel on Climate Change, lead to a global overestimation in the uncertainty in regional sea‐level projections on the order of 20%. Regionally, this overestimation exceeds 100%. We conclude that accurate uncertainty estimates of regional sea‐level change must be based on projections of regional climate change and cannot be derived from global indicators such as global mean surface temperature.

Climate Change and Health: Strengthening the Evidence Base for Policy

The main objective of this study was to understand the impacts of climate variability and change on coastal resource governance, communities’ vulnerability and livelihoods security in the district. Specifically, the study aimed to assess the effects of rainfall and temperature variability and change on coastal resource distribution and management, to examine the vulnerabilities of socio-economic activities along the coastal areas to the impacts of climate variability and change and to establish the adaptive measures that the community and other stakeholders adapted to address the impacts of climate change and variability. The research was conducted in three purposively selected villages in Pangani District. A random sample of 60 respondents participated in the study. The study employed a mixture of quantitative and qualitative methods. Findings from this study demonstrate that climate change has happened and is still happening in the study area. The notable impacts of climate change include decrease of rainfall and change of rainfall seasons and patterns, emergence of new crop diseases and pests, decrease of fish catch, intrusion of salinity in the rivers and inland and general decline of crop productivity. The findings reveal that the impacts of climate change have been increasing over time between 1980 and 2014.

The Heat is On: Imperative for Midwifery Engagement in Climate Change.

Introduction There is a substantial literature about the future impacts of climate change (Nordhaus 1991; Cline 1992b; Fankhauser 1995; Mendelsohn et al . 2000; Tol 2002a); see Tol (2009b) for an overview. Less is known, however, about the impacts of climate change in the past. While there is no immediate policy relevance of estimates of past effects – as liability is yet to be established (Tol and Verheyen 2004) – such estimates would serve to validate models of future impacts – and thus help to improve these models and build confidence. In this chapter, I turn this question on its head. I use a model to backcast past impacts, thus generating hypotheses to be tested against observations. Unfortunately, there are no direct observations of the economic impact of past climate change. Note that the cause of climate change, past or future, is irrelevant for its impacts. There are, however, some studies that estimate particular aspects of the impact of past climate change, typically focussing on biophysical impacts.

The last 10 years have been the warmest in the Arctic during the 120-year period of instrumental observations. The global mean surface temperature during that period has increased by about 0.8°C, with stronger changes in the Arctic. Retreat of the Arctic sea ice during the past decades open an additional source of heat and water vapor in the autumn and early winter seasons. If these warming trends continue, they will significantly affect the Arctic land cover and land use, also causing impacts on the global scale. The changes will occur in the natural land cover, with perhaps the greatest effects in that part of the Arctic where the land cover has already been modified by human activities. In many Arctic areas there has been a clear shift from the land use practiced by indigenous peoples to intensive exploitation of the land for commercial and industrial uses. New navigation routes across the Arctic Ocean shelf seas are broadly discussed. If and when implemented, these routes will change the Arctic land use and the life style of the population. The International Polar Year (IPY) program involving over 200 projects with thousands of scientists from over 60 nations is coming to its final stage. This book is a compilation of the studies which have been conducted in the framework of the NASA Land-Cover/Land-Use Change Program and which have been focused on the Arctic region of Northern Eurasia, although some comparisons are made with the results in North America. The region of interest in the current book is north of 60° latitude, specifically transitional forest-tundra and tundra zones.

Purpose The purpose of this study is to analyze the impact of climate change and variability on agricultural production in Middle East and North Africa region (MENA) where the deleterious impacts of climate change are generally projected to be greatest. Design/methodology/approach The study used a production function model using Fixed Effect Regression (FER) analysis and then using marginal impact analysis to assess the impact of climate change and variability on agricultural production. Therefore, the study utilized panel data for the period 1961-2009 pooled from 20 countries in MENA region. Findings Results showed that 1 per cent increase in temperature during winter resulted in 1.12 per cent decrease in agricultural production. It was also observed that 1 per cent increase in temperature variability during winter and spring resulted in 0.09 and 0.14 per cent decrease in agricultural production, respectively. Results also indicated that increasing precipitation during winter and fall season and precipitation variability during winter and summer seasons had negative impact. The estimated parameters of square temperature and precipitation indicated that climate change has significant nonlinear impacts on agricultural production in MENA region. Originality/value Despite there are many studies on the impact of climate change on agricultural production, there is a lack of publications to address the economic impact of both climate change and variability on agricultural production in MENA region. Thus, these results are more comprehensive and more informative to policymakers than the results from field trials.

Reconstructing global mean surface temperature (GMST) is one of the key contributions that paleoclimate science can make in addressing societally relevant questions and is required to determine equilibrium climate sensitivity (ECS). GMST has been derived from the temperature of the deep ocean (Td), with previous work suggesting a simple Td‐GMST scaling factor of 1 prior to the Pliocene. However, this factor lacks a robust mechanistic basis, and indeed, is intuitively difficult to envisage given that polar amplification is a ubiquitous feature of past warm climate states and deep water overwhelmingly forms at high latitudes. Here, we interrogate whether and crucially, why, this relationship exists using a suite of curated data compilations and two sets of paleoclimate model simulations. We show that models and data are in full agreement that a 1:1 relationship is a good approximation. Taken together, the two sets of climate models suggest that (a) a lower sensitivity of SST in the season of deep water formation than high latitude mean annual SST in response to climate forcing, and moreover (b) a greater degree of land versus ocean surface warming are the two processes that act to counterbalance a possible polar amplification‐derived bias on Td‐derived GMST. Using this knowledge, we provide a new Cenozoic record of GMST. Our estimates are substantially warmer than similar previous efforts for much of the Paleogene and are thus consistent with a substantially higher‐than‐modern ECS during deep‐time high CO2 climate states.

Global mean surface temperature (GMST) rising has slowed down since late 1990s, which is referred to as the global warming hiatus. There was another global warming hiatus event during 1940s–1960s. The roles of the external forcing and the natural variability in both global warming hiatuses are explored, using EOF analysis. The first two leading EOF modes of the 5-year running mean global sea surface temperature (SST) reflect the global warming scenario (EOF1) and the interdecadal Pacific oscillation (IPO)-like natural variability (EOF2), respectively. In observation, PC2 was in its positive phase (eastern Pacific cooling) during 1940s–1960s, which contributed to the previous warming hiatus. In addition, GMST trends are found to be negative during late 1950s and 1960s in most of the CMIP5 historical runs, which implies that the external forcing also contributed to the pause in the GMST rising. It is further demonstrated that it is the natural radiative forcing (volcanic forcing) that caused the drop-down of GMST in 1960s. The current global warming hiatus has been attributed to the eastern Pacific cooling/enhanced Pacific trade winds. It is shown that the PC2 switched to its positive phase in late 1990s, and hence the IPO-like natural variability made a contribution to the slowdown of GMST rising in the past decade. It is also found that the EOF1 mode (global warming mode) of the observed SST features a smaller warming in tropical Pacific compared to the Indian Ocean and the tropical Atlantic. Such inter-basin warming contrast, which is attributed to the “ocean thermostat” mechanism, has been suggested to contribute to the intensification of Pacific trade winds since late 1990s as well. Global warming hiatuses are also found in the future projections from CMIP5 models, and the spatial pattern of the SST trends during the warming-hiatus periods exhibits an IPO-like pattern, which resembles the observed SST trends since late 1990s.

Can climate sensitivity be estimated from short-term relationships of top-of-atmosphere net radiation and surface temperature?