Revisiting the Holocene global temperature conundrum.

Recent global temperature reconstructions for the current interglacial period (the Holocene, beginning 11,700 years ago) have generated contrasting trends. This Review examines evidence from indicators and drivers of global change, as inferred from proxy records and simulated by climate models, to evaluate whether anthropogenic global warming was preceded by a long-term warming trend or by global cooling. Multimillennial-scale cooling before industrialization requires extra climate forcing and major climate feedbacks that are not well represented in most climate models at present. Conversely, global warming before industrialization challenges proxy-based reconstructions of past climate. The resolution of this conundrum has implications for contextualizing post-industrial warming and for understanding climate sensitivity to several forcings and their attendant feedbacks, including greenhouse gases. From a large variety of available evidence, we find support for a relatively mild millennial-scale global thermal maximum during the mid-Holocene, but more research is needed to firmly resolve the conundrum and to advance our understanding of slow-moving climate variability.

A data integration framework for spatial interpolation of temperature observations using climate model data.

Meteorological station measurements are an important source of information for understanding the weather and its association with risk, and are vital in quantifying climate change. However, such data tend to lack spatial coverage and are often plagued with flaws such as erroneous outliers and missing values. Alternative meteorological data exist in the form of climate model output that have better spatial coverage, at the expense of bias. We propose a probabilistic framework to integrate temperature measurements with climate model (reanalysis) data, in a way that allows for biases and erroneous outliers, while enabling prediction at any spatial resolution. The approach is Bayesian which facilitates uncertainty quantification and simulation based inference, as illustrated by application to two countries from the Middle East and North Africa region, an important climate change hotspot. We demonstrate the use of the model in: identifying outliers, imputing missing values, non-linear bias correction, downscaling and aggregation to any given spatial configuration.

Climate change multi-model projections in CMIP6 scenarios in Central Hokkaido, Japan.

Simulation of future climate changes, especially temperature and rainfall, is critical for water resource management, disaster mitigation, and agricultural development. Based on the category-wise indicator method, two preferred Global Climate Models (GCMs) for the Ishikari River basin (IRB), the socio-economic center of Hokkaido, Japan, were examined from the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6). Climatic variables (maximum/minimum temperature and precipitation) were projected by the Statistical DownScaling Model (SDSM) under all shared socioeconomic pathway-representative concentration pathway (SSP-RCP) scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, SSP5-3.4OS, and SSP5-8.5) in two phases: 2040-2069 (2040s) and 2070-2099 (2070s), with the period of 1985-2014 as the baseline. Predictors of SDSM were derived from CMIP6 GCMs and the reanalysis dataset NOAA-CIRES-DOE 20th Century Reanalysis V3 (20CRv3). Results showed that CMIP6 GCMs had a significant correlation with temperature measurements, but could not represent precipitation features in the IRB. The constructed SDSM could capture the characteristics of temperature and precipitation during the calibration (1985-1999) and validation (2000-2014) phases, respectively. The selected GCMs (MIROC6 and MRI-ESM-2.0) generated higher temperature and less rainfall in the forthcoming phases. The SSP-RCP scenarios had an apparent influence on temperature and precipitation. High-emission scenarios (i.e., SSP5-8.5) would project a higher temperature and lower rainfall than the low-emission scenarios (e.g., SSP1-1.9). Spatial-temporal analysis indicated that the northern part of the IRB is more likely to become warmer with heavier precipitation than the southern part in the future. Higher temperature and lower rainfall were projected throughout the late twenty-first century (2070s) than the mid-century (2040s) in the IRB. The findings of this study could be further used to predict the hydrological cycle and assess the ecosystem's sustainability.

Population exposure to drought severities under shared socioeconomic pathways scenarios in India.

As a widespread natural hazard, droughts impact several aspects of human society adversely. Thus, the present study aims to answer the following research questions; (i) What are the expected variabilities in different drought conditions over India in the future? (ii) How the population exposure to drought varies under different climate change and population scenarios? (iii) How is the total exposure attributed to the individual exposure (climate, population, and interaction) in future climate change scenarios? In this sense, the study is performed under four Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) using thirteen Global Climate Models from Coupled Model Intercomparison Project Phase 6 and Standardized Precipitation Evapotranspiration Index as a drought indicator. The future period is divided into two parts i.e., 2023-2061 (T1) and 2062-2100 (T2), and compared with the historical period during 1967-2005. The results show that the severe (56 % to 72 % of the area) and extreme (99 % of the area) droughts are likely to increase under all the scenarios for 3-month scale conditions, respectively. The drought intensity is projected to increase under 3-and 12-month scale drought conditions. The population exposure to the extreme drought severity is anticipated to increase for both the drought conditions and the highest exposure is noticed under the SSP3-7.0 scenario. The significant contribution from climate or interaction effects is observed in the case of 3- and 9-month scale extreme drought conditions. The present study necessitates a call for effective measures to alleviate the risk, especially in the high-risk areas of India.

The missing risks of climate change.

The risks of climate change are enormous, threatening the lives and livelihoods of millions to billions of people. The economic consequences of many of the complex risks associated with climate change cannot, however, currently be quantified. Here we argue that these unquantified, poorly understood and often deeply uncertain risks can and should be included in economic evaluations and decision-making processes. We present an overview of these unquantified risks and an ontology of them founded on the reasons behind their lack of robust evaluation. These consist of risks missing owing to delays in sharing knowledge and expertise across disciplines, spatial and temporal variations of climate impacts, feedbacks and interactions between risks, deep uncertainty in our knowledge, and currently unidentified risks. We highlight collaboration needs within and between the natural and social science communities to address these gaps. We also provide an approach for integrating assessments or speculations of these risks in a way that accounts for interdependencies, avoids double counting and makes assumptions clear. Multiple paths exist for engaging with these missing risks, with both model-based quantification and non-model-based qualitative assessments playing crucial roles. A wide range of climate impacts are understudied or challenging to quantify, and are missing from current evaluations of the climate risks to lives and livelihoods. Strong interdisciplinary collaboration and deeper engagement with uncertainty is needed to properly inform policymakers and the public about climate risks.

Statistical downscaling of sea levels: application of multi-criteria analysis for selection of global climate models.

Sea level rise is one of the serious aftermaths of global warming on the hydrosphere. The scientific community often depends on global climate models (GCMs) for projection of future sea levels. Numerous GCMs are available; thus, selecting the most appropriate GCM/GCMs is a critical task to be performed prior to downscaling. In this study, multi-criteria decision-making (MCDM) techniques, namely, Preference Ranking Organisation Method of Enrichment Evaluation (PROMETHEE-II), Elimination Et Choice Translating Reality (ELECTRE-II), and compromise programming, were used to identify appropriate GCMs whose projections can be used to downscale sea level projections at Ernakulam, Kerala, India. Support vector machine was employed to statistically downscale the sea level projections from the projections of GCMs. Five statistical metrics, namely, correlation coefficient ([Formula: see text]), normalized root mean square error, absolute normalized average bias, mean absolute relative error, and skill score, were adopted in this study as the performance criteria. The weightage of each criterion was computed using the entropy method. Six GCMs (GISS-E2-H, CanESM2, ACCESS1-0, CNRM-CM5, GFDL-CM3, and CMCC-CM) were considered for the analysis based on the availability of predictors. GISS-E2-H, CanESM2, and ACCESS1-0 occupied the first three positions respectively in all three MCDM techniques.

Comprehensive evidence implies a higher social cost of CO2.

The social cost of carbon dioxide (SC-CO2) measures the monetized value of the damages to society caused by an incremental metric tonne of CO2 emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit-cost analysis for over a decade, SC-CO2 estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine1 (NASEM) highlighted that current SC-CO2 estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency and uncertainty characterization of SC-CO2 estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO2. Our preferred mean SC-CO2 estimate is $185 per tonne of CO2 ($44-$413 per tCO2: 5%-95% range, 2020 US dollars) at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US government's current value of $51 per tCO2. Our estimates incorporate updated scientific understanding throughout all components of SC-CO2 estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO2 values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.

Sufficient conditions for rapid range expansion of a boreal conifer.

Unprecedented modern rates of warming are expected to advance boreal forest into Arctic tundra1, thereby reducing albedo2-4, altering carbon cycling4 and further changing climate1-4, yet the patterns and processes of this biome shift remain unclear5. Climate warming, required for previous boreal advances6-17, is not sufficient by itself for modern range expansion of conifers forming forest-tundra ecotones5,12-15,17-20. No high-latitude population of conifers, the dominant North American Arctic treeline taxon, has previously been documented5 advancing at rates following the last glacial maximum (LGM)6-8. Here we describe a population of white spruce (Picea glauca) advancing at post-LGM rates7 across an Arctic basin distant from established treelines and provide evidence of mechanisms sustaining the advance. The population doubles each decade, with exponential radial growth in the main stems of individual trees correlating positively with July air temperature. Lateral branches in adults and terminal leaders in large juveniles grow almost twice as fast as those at established treelines. We conclude that surpassing temperature thresholds1,6-17, together with winter winds facilitating long-distance dispersal, deeper snowpack and increased soil nutrient availability promoting recruitment and growth, provides sufficient conditions for boreal forest advance. These observations enable forecast modelling with important insights into the environmental conditions converting tundra into forest.

Warm springs alter timing but not total growth of temperate deciduous trees.

As the climate changes, warmer spring temperatures are causing earlier leaf-out1-3 and commencement of CO2 uptake1,3 in temperate deciduous forests, resulting in a tendency towards increased growing season length3 and annual CO2 uptake1,3-7. However, less is known about how spring temperatures affect tree stem growth8,9, which sequesters carbon in wood that has a long residence time in the ecosystem10,11. Here we show that warmer spring temperatures shifted stem diameter growth of deciduous trees earlier but had no consistent effect on peak growing season length, maximum growth rates, or annual growth, using dendrometer band measurements from 440 trees across two forests. The latter finding was confirmed on the centennial scale by 207 tree-ring chronologies from 108 forests across eastern North America, where annual ring width was far more sensitive to temperatures during the peak growing season than in the spring. These findings imply that any extra CO2 uptake in years with warmer spring temperatures4,5 does not significantly contribute to increased sequestration in long-lived woody stem biomass. Rather, contradicting projections from global carbon cycle models1,12, our empirical results imply that warming spring temperatures are unlikely to increase woody productivity enough to strengthen the long-term CO2 sink of temperate deciduous forests.

Even modest climate change may lead to major transitions in boreal forests.

The sensitivity of forests to near-term warming and associated precipitation shifts remains uncertain1-9. Herein, using a 5-year open-air experiment in southern boreal forest, we show divergent responses to modest climate alteration among juveniles of nine co-occurring North American tree species. Warming alone (+1.6 °C or +3.1 °C above ambient temperature) or combined with reduced rainfall increased the juvenile mortality of all species, especially boreal conifers. Species differed in growth responses to warming, ranging from enhanced growth in Acer rubrum and Acer saccharum to severe growth reductions in Abies balsamea, Picea glauca and Pinus strobus. Moreover, treatment-induced changes in both photosynthesis and growth help explain treatment-driven changes in survival. Treatments in which species experienced conditions warmer or drier than at their range margins resulted in the most adverse impacts on growth and survival. Species abundant in southern boreal forests had the largest reductions in growth and survival due to climate manipulations. By contrast, temperate species that experienced little mortality and substantial growth enhancement in response to warming are rare throughout southern boreal forest and unlikely to rapidly expand their density and distribution. Therefore, projected climate change will probably cause regeneration failure of currently dominant southern boreal species and, coupled with their slow replacement by temperate species, lead to tree regeneration shortfalls with potential adverse impacts on the health, diversity and ecosystem services of regional forests.

Response of the East Antarctic Ice Sheet to past and future climate change.

The East Antarctic Ice Sheet contains the vast majority of Earth's glacier ice (about 52 metres sea-level equivalent), but is often viewed as less vulnerable to global warming than the West Antarctic or Greenland ice sheets. However, some regions of the East Antarctic Ice Sheet have lost mass over recent decades, prompting the need to re-evaluate its sensitivity to climate change. Here we review the response of the East Antarctic Ice Sheet to past warm periods, synthesize current observations of change and evaluate future projections. Some marine-based catchments that underwent notable mass loss during past warm periods are losing mass at present but most projections indicate increased accumulation across the East Antarctic Ice Sheet over the twenty-first century, keeping the ice sheet broadly in balance. Beyond 2100, high-emissions scenarios generate increased ice discharge and potentially several metres of sea-level rise within just a few centuries, but substantial mass loss could be averted if the Paris Agreement to limit warming below 2 degrees Celsius is satisfied.

The inequality labor loss risk from future urban warming and adaptation strategies.

Heat-induced labor loss is a major economic cost related to climate change. Here, we use hourly heat stress data modeled with a regional climate model to investigate the heat-induced labor loss in 231 Chinese cities. Results indicate that future urban heat stress is projected to cause an increase in labor losses exceeding 0.20% of the total account gross domestic product (GDP) per year by the 2050s relative to the 2010s. In this process, certain lower-paid sectors could be disproportionately impacted. The implementation of various urban adaptation strategies could offset 10% of the additional economic loss per year and help reduce the inequality-related impact on lower-paid sectors. So future urban warming can not only damage cities as a whole but can also contribute to income inequality. The implication of adaptation strategies should be considered in regard to not only cooling requirements but also environmental justice.

Climate Changes Exacerbate the Spread of Ixodes ricinus and the Occurrence of Lyme Borreliosis and Tick-Borne Encephalitis in Europe-How Climate Models Are Used as a Risk Assessment Approach for Tick-Borne Diseases.

Climate change has influenced the transmission of a wide range of vector-borne diseases in Europe, which is a pressing public health challenge for the coming decades. Numerous theories have been developed in order to explain how tick-borne diseases are associated with climate change. These theories include higher proliferation rates, extended transmission season, changes in ecological balances, and climate-related migration of vectors, reservoir hosts, or human populations. Changes of the epidemiological pattern have potentially catastrophic consequences, resulting in increasing prevalence of tick-borne diseases. Thus, investigation of the relationship between climate change and tick-borne diseases is critical. In this regard, climate models that predict the ticks' geographical distribution changes can be used as a predicting tool. The aim of this review is to provide the current evidence regarding the contribution of the climatic changes to Lyme borreliosis (LB) disease and tick-borne encephalitis (TBE) and to present how computational models will advance our understanding of the relationship between climate change and tick-borne diseases in Europe.

A generalized methodology for ranking climate models based on climate indices for sector-specific studies: An application to the Mekong sub-basin.

This study proposes a methodological framework to evaluate and rank climate models based on extreme climate indices of precipitation and temperature for impact studies in seven sectors: Cryosphere, Energy, Forestry/GHGs, Health, Agriculture & Food Security, Disaster Risk Reduction (flood and drought), and Water Resources & Hydrology. The ranking of the climate models is based on their performance in sector-relevant extreme climate indices. Extreme climate indices for observed and climate models' datasets for a historical period and overall performance statistics were used to create a payoff matrix. The payoff matrix then served as an input to a multi-criteria decision-making process to rank the climate models for each of the climate indices. The final sector-specific ranking was achieved by averaging the ranks obtained in the sector-relevant indices. The developed methodology is demonstrated with an application to the Songkhram River Basin (Thailand), a sub-basin of the Mekong. Eighteen CMIP6 GCMs are used for the proposed evaluation and ranking processes and four performance statistics were used. Weights to each of the four performance statistics were determined using the entropy method. Compromise programming was applied to rank the GCMs based on the distance technique. The results indicate that the six best performing models are different for different sectors, with the GFDL_CM4 model common in all the seven sectors considered in the study. KACE1_0_G, GFDL_ESM4, GFDL_CM4, MRI_ESM2_0, and ACCESS_ESM1_5 models are the five top (ranked 1 to 5 respectively) performing models for the Water Resources & Hydrology sector. The developed framework is generic and can be applied to any region or basin; at the same time, it can also provide researchers and policymakers with specific information on best-performing models for particular sectors.

Observed poleward freshwater transport since 1970.

Warming-induced global water cycle changes pose a significant challenge to global ecosystems and human society. However, quantifying historical water cycle change is difficult owing to a dearth of direct observations, particularly over the ocean, where 77% and 85% of global precipitation and evaporation occur, respectively1-3. Air-sea fluxes of freshwater imprint on ocean salinity such that mean salinity is lowest in the warmest and coldest parts of the ocean, and is highest at intermediate temperatures4. Here we track salinity trends in the warm, salty fraction of the ocean, and quantify the observed net poleward transport of freshwater in the Earth system from 1970 to 2014. Over this period, poleward freshwater transport from warm to cold ocean regions has occurred at a rate of 34-62 milli-sverdrups (mSv = 103 m3 s-1), a rate that is not replicated in the current generation of climate models (the Climate Model Intercomparison Project Phase 6 (CMIP6)). In CMIP6 models, surface freshwater flux intensification in warm ocean regions leads to an approximately equivalent change in ocean freshwater content, with little impact from ocean mixing and circulation. Should this partition of processes hold for the real world, the implication is that the historical surface flux amplification is weaker (0.3-4.6%) in CMIP6 compared with observations (3.0-7.4%). These results establish a historical constraint on poleward freshwater transport that will assist in addressing biases in climate models.