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ABSTRACT The Coupled Model Inter‐comparison Project phase 6 (CMIP6) provides a suite of general circulation models (GCMs) and Socioeconomic Shared Pathways (SSPs) primarily for continental‐scale climate assessments. However, adapting these models for sub‐national assessments, particularly in countries with varied geography like Ecuador, and for complex variables such as precipitation, introduces challenges, including uncertainties in selecting appropriate GCMs and SSPs. To address these issues, we adopt a biogeographical approach that integrates regional climatic variations. Our analysis explores 26 GCMs, four SSP scenarios and four 20‐year time frames from WorldClim to evaluate discrepancies between the GCM precipitation projections, historical data and national climate projections across five Ecuadorian bioregions. This approach enabled us to sort the GCMs by annual precipitation medians, classify their monthly precipitation using Dynamic Time Warping (DTW) clustering, and develop ensembles highlighting both the largest and average precipitation anomalies within and beyond the bioregions. Among the 26 models examined, 16 projected an increase in annual precipitation in Ecuador, especially during the wet seasons, with the BCC‐CSM2‐MR model showing peak values, notably in the Choco region and eastern Amazon basin. Conversely, 10 models, with CMCC‐ESM2 showing the largest decreases, projected reduced precipitation across almost all Ecuadorian territories, except the Choco region. The largest reductions were in the Amazon basin, raising concerns about reduced precipitation. Discrepancies, primarily in the Andes and Galapagos bioregions, reveal the challenges posed by their complex topography and insular environments. While the GCMs captured spatial patterns of ENSO, our research was constrained to 20‐year averages, making direct comparison with historical records infeasible, highlighting the need for further research with shorter time frames and finer spatial resolutions. The variability in precipitation was linked to geographical factors, GCM configurations and unexpected SSP outcomes. Therefore, selecting GCMs and climatic indices tailored to specific bioregions is recommended for effective climate change impact assessments. [ABSTRACT FROM AUTHOR]

The Last Glacial Maximum (LGM; 26,000–19,000 years before the present) altered the distribution of species worldwide. Its effect is poorly known in tropical regions because the cooling and drought reached during that period are uncertain. Here, I generated hypotheses regarding the possible responses of cloud forest mammals in the Oaxacan Highlands (OH) of Mexico, a region with one of the most extensive cloud forests in the Neotropics. First, I used three General Circulation Models (GCMs: CCSM3, MIROC‐ESM and MPI) to characterize probable climates during the LGM. Then, I used ecological niche models to estimate the current and LGM potential distributions of four cloud forest species. As in other locations, the results show that GCMs are consistent with cooler conditions relative to today; however, the three GCMs estimate precipitation regimes with notable variations in the region. MPI indicates that the LGM could have been even wetter than present. Consequently, the MPI scenario allowed more widespread potential distributions of mammals. The paleodistributions show how mid‐ and lowlands were essential for the long‐term survival of these 'high‐mountain mammals' throughout the last glacial–interglacial cycle. The paleodistributions presented here are precise hypotheses that can be tested based on paleoecological and genetic evidence. [ABSTRACT FROM AUTHOR]

The last few decades have shown evidence of a lengthening dry season in southern Amazonia, which is associated with a delay in the onset of the South American Monsoon System (SAMS). Using a pattern recognition framework of atmospheric circulation patterns (CPs), previous studies have identified specific atmospheric situations related to the onset of the SAMS. Here, we analyze the future changes in the CPs that largely define the main hydro‐climatological states of Tropical South America. We evaluated the CP changes that occurred between two periods: historical (1970–2000) and future (2040–2070), using six General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 6. Future GCM projections show significant spatio‐temporal changes in the CPs associated with the dry season in southern Amazonia during the mid‐21st century. These changes are related to both a late onset of the SAMS and an early demise of the SAMS. Particularly, the CP methodology allowed for a better understanding of the behavior of the southern Amazon dry season under future conditions, showing an increase in the frequency of the CPs typically observed during the dry season. The occurrence of dry days in the Amazon basin during the austral winter of the mid‐21st century increases by 19.4% on average, with respect to the historical period. This methodology also identified a future increase in the frequency of dry CPs, both at the beginning of the dry‐to‐wet transition period (8%) and at the end of the wet‐to‐dry transition season (11%). Plain Language Summary: The southern Amazon has experienced a lengthening of the dry season over the past few decades. This trend has negative impacts on ecological services offered by the Amazon basin, affecting not only the region itself but also the entire planet. This is due to the fact that the Amazon basin plays a critical role in preserving the global hydrological and energy balance. We identify the future changes in the main atmospheric states in South Tropical South America, called atmospheric circulation patterns (CPs). These CPs can be interpreted as an average of each dominant meteorological situation over the region. For this purpose, we evaluated the projected changes occurring in the CPs considering two periods: historical (1970–2000) and future (2040–2070), using six global models. Our results show temporal and spatial variations in the CPs associated with the dry season during the mid‐21st century. These changes are related to both a late dry season ending and an early dry season arrival. For the future period we detect an increase in the frequency of the CPs typically observed during the dry season (19% higher than historical period). This is particularly important in a region already threatened by land use and climate change. Key Points: Circulation Patterns (CPs) provide a new perspective of the lengthening of the dry season in southern AmazoniaGeneral circulation models (GCMs) show significant future changes in the main dry‐to‐wet transitional atmospheric statesGCMs indicate a projected increase in the frequency of CPs typically observed during the Amazon dry season by the mid‐21st century [ABSTRACT FROM AUTHOR]

With the development of advanced data assimilation and computing techniques, many modern global reanalysis datasets aim to resolve the atmospheric mesoscale spectrum. However, large uncertainties remain with respect to the representation of mesoscale motions in these reanalysis datasets, for which a clear understanding is lacking. The aforementioned challenges have served as a strong motivation to reveal and quantify their mesoscale differences. This study presents the first comprehensive global intercomparison of the tropospheric and stratospheric mesoscale kinetic energy and its spectra over two selected periods of summer and winter events among six leading high-resolution atmospheric reanalysis products: European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), China Meteorological Administration Reanalysis, Modern-Era Retrospective Analysis for Research and Applications version 2, National Centers for Environmental Prediction's Climate Forecast System version 2 (CFSv2), Japanese 55-year Reanalysis, and ECMWF Reanalysis-Interim. A state-of-the-art global operational model is adopted as a supplementary reference. Although all reanalysis datasets can reproduce broad distribution characteristics that are grossly consistent with the 9 km model, there are substantial discrepancies among them in magnitudes. The ability to capture mesoscale signals is closely linked to their resolutions, but it is also impacted by other factors, including, but not limited to, the selected types of energy, seasons, altitudes, latitudes, model diffusions, parametrization schemes, moist condition, assimilation methods, and observation inputs. Moreover, all datasets illustrate conclusive behaviors for the prevalence of the rotational component in the troposphere, whereas only very few products fail to exhibit the dominance of the divergent component in the stratosphere. Overall, stratospheric ERA5 and CFSv2 outperform the other reanalysis datasets, and only these two can reproduce the feature of the canonical kinetic energy spectrum with a distinct shift from a steeper slope (approximately -3) at lower wave numbers to a shallower slope (approximately -5/3) at higher wave numbers. In addition, the relative disparities among datasets increase dramatically with height, and they are more pronounced in the divergent component. It is also found that the correlations among these datasets are much weaker in the Tropics. [ABSTRACT FROM AUTHOR]

Projecting precipitation changes is essential for researchers to understand climate change impacts on hydrological cycle. This study projected future precipitation over the Hanjiang River Basin (HRB) based on the multimodel ensemble (ME) of six global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). An ME method using the fusion of four machine learning (ML) algorithms (random forest [RF], K‐nearest neighbors [KNN], extra tree [ET], and gradient boosting decision tree [GBDT]) was proposed in this study. The future precipitation changes were investigated during 2023–2042 (Near‐term), 2043–2062 (Mid‐term), and 2081–2100 (Long‐term) periods, with reference to the base period 1995–2014, under three integrated scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5) of the Shared Socioeconomic Pathways (SSPs) and the representative concentration pathways (RCPs). The results show that: (1) the proposed ME method performs better than the ME mean and individual ML algorithms, with a correlation coefficient value reaching 0.88 and Taylor skill score reaching 0.764. (2) The precipitation under SSP5‐8.5 has the largest upward trend with the annual precipitation variation range of −9.27% to 112.84% from 2023 to 2100, followed by SSP2‐4.5 with −30.48% to 44.67%, and the smallest under SSP1‐2.6 with −37.19% to 37.78%, which show a significant trend of humidification over the HRB in the future. (3) The precipitation changes over the HRB are projected to increase over time, with the largest in the Long‐term, followed by Mid‐term, and the smallest in the Near‐term. (4) The northeastern parts of the HRB are projected to experience a large precipitation in the future, and the southeastern parts are smaller. (5) Uncertainties in the projected precipitation over the HRB still exist, which can be reduced by ME. The findings obtained in this study have important implications for hydrological policymakers to make adaptive strategies to reduce the risks of climate change. [ABSTRACT FROM AUTHOR]

Motivation: Climate change plays an important role in the generation and maintenance of biodiversity by driving processes such as diversification and range shifts. As a result, biodiversity patterns are often found to carry the imprints of palaeoclimatic changes. However, we still know little about the spatial and temporal variation in climate over the scale of millennia affecting eco‐evolutionary dynamics, mostly because of the scarcity of user‐friendly and freely available spatio‐temporal palaeoclimate series at such temporal scales. Here, we address this gap by presenting PALEO‐PGEM‐Series, a global spatio‐temporal dataset of the last 5 Myr, with 1 kyr resolution, spatially downscaled from emulations performed with the intermediate‐complexity atmosphere–ocean general circulation model PALEO‐PGEM. PALEO‐PGEM‐Series holds the potential to advance our understanding of the mechanisms behind the strong relationship between biodiversity and climate, a pressing need given projected biodiversity responses to anthropogenic climatic change. Main Types of Variables Contained: Spatio‐temporal series of monthly temperature and precipitation and 17 derived bioclimatic variables over the Pliocene–Pleistocene, along with standard error estimates from multiple runs of the emulator. Spatial Location and Grain: Global landmasses, at 1° × 1°. Time Period and Grain: Last 5 Myr at 1000 year resolution. Major Taxa and Level of Measurement: Not applicable. Software Format: Tab‐delimited text files and accompanying R code to derive bioclimatic variables. [ABSTRACT FROM AUTHOR]

Historical simulations and projections of climate extremes indices of precipitation and temperature were analysed over South America until the end of the 21st century through 31 general circulation models (GCMs) under four Representative Concentration Pathways. Simulations were compared with reanalysis data, and a Bayesian inference method was used to assess the uncertainties involved in the multi‐model climate projections. Regarding the precipitation extremes indices, the GCMs' simulations reasonably approached the reanalysis data, but with heterogeneous biases, both in sign and in the location of the highest values. The temperature extremes indices presented the smallest biases when compared to precipitation. Projections show a gradual growth of precipitation extremes events as the analysed radiative forcing scenario increases, both in magnitude and extent, over a large part of South America. Projections also indicate a decrease in cold days and nights and an increase in warm days and nights, more pronounced in the equatorial region. Bayesian inference method smoothed changes in precipitation extremes events, both in magnitude and extent, compared to the simple GCMs' ensemble mean. There was no considerable variation in the temperature indices when applying the Bayesian inference. Finally, the probability density functions resulted in a predominance of multimodal and wide curves for the precipitation indices, showing great uncertainties in the GCMs' results, differently from those for the temperature indices, where the GCMs presented good agreement represented through unimodal and narrow curves. [ABSTRACT FROM AUTHOR]

This study evaluates the simulation of the seasonal cycle of water isotopic composition over Tibetan Plateau regions (TP) from six isotope‐enabled general circulation models (GCMs) participating in the second Phase of Stable Water Isotope Intercomparison Group. For both meteorological factors (precipitation rate and wind field) and isotopic composition, GCMs generally agree with reanalysis data and in‐situ observations, but there is a significant spread across models and the isotopic seasonality is systematically underestimated. In the southern TP, the precipitation isotopic composition is more depleted in summer than in winter, and the amplitude of the simulated isotopic seasonal variations is primarily driven by the amplitude of the simulated upstream precipitation. In contrast, in the northern TP, the precipitation isotopic composition is more depleted in winter than in summer, and the amplitude of the simulated seasonal variability of isotopes is mainly driven by the simulated strength of the zonal wind. We conclude that the skill of a GCM to simulate the seasonal cycle in the isotopic composition depends mainly on the skill of the GCM to simulate the Indian summer monsoon precipitation and the westerlies. The same causes contributing to the underestimated seasonality at present‐day may also contribute to the underestimated δ18O change at the mid‐Holocene. Plain Language Summary: The stable isotopic composition of water is determined by the relative abundances of heavier to lighter isotopologues. Isotope‐enabled general circulation models (GCMs) simulate water isotope composition considering several physical processes operating in the hydrologic cycle. Therefore, a comparison between observed and simulated values provides a better understanding of the processes constraining the isotope values. However, the simulated water isotopic composition is sensitive to various physical processes and parameterization schemes. In this study, we investigate how the model biases in the representation of atmospheric processes affect the simulation of isotopic composition over the Tibetan Plateau (TP). We compare the simulated isotopic composition in precipitation and water vapor to in‐situ and satellite measurements. Our analysis indicates that most GCMs underestimate the seasonality of the isotopic composition in the precipitation and vapor. In the southern TP, the spread of upstream precipitation is an important factor controlling the inter‐model spread in isotopic seasonality. In the northern TP, the westerlies majorly control isotope biases. Key Points: Models systematically underestimate the precipitation isotopic seasonality both in the southern and northern TPThe inter‐model spread of precipitation isotopic seasonality in the southern TP is driven by the spread of upstream precipitationThe westerlies dominate the inter‐model spread of precipitation isotopic seasonality in the northern TP [ABSTRACT FROM AUTHOR]

Current state‐of‐the art procedures for studying modeled submesoscale oceanographic features have made a strong assumption of independence between features identified at different times. Therefore, all submesoscale eddies identified in a time series were studied in aggregate. Statistics from these methods are illuminating but oversample identified features and cannot determine the lifetime evolution of the transient submesoscale processes. To this end, the authors apply the Topological Feature Tracking (TFT) algorithm to the problem of identifying and tracking submesoscale eddies over time. TFT identifies critical points on a set of time‐ordered scalar fields and associates those points between consecutive timesteps. The procedure yields tracklets which represent spatio‐temporal displacement of eddies. In this way we study the time‐dependent behavior of submesoscale eddies, which are generated by a 1‐km resolution submesoscale‐permitting model. We summarize the submesoscale eddy data set produced by TFT, which yields unique, time‐varying statistics. Plain Language Summary: Current state‐of‐the art procedures for studying small‐scale features in the ocean do not take the effects of time into account. Instead, features like small vortices are studied as a single population across many points in time. This method has provided oceanographers with many valuable insights. New insights can be added by identifying vortices and then tracking them over time to study their behavior through an algorithm designed to identify and track features on a grid. Key Points: Topological Feature Tracking is introduced as a way to identify features in scalar fields and associate those features through timeWe identify and track submesoscale eddies over 1‐year of ocean surface velocity data computed via the Navy Coastal Ocean ModelEddy statistics provide insight on lifetime, speed, and distance traversed for understanding eddy motions and scale interactions [ABSTRACT FROM AUTHOR]

The representation of the South American Monsoon System (SAMS) by general circulation models (GCMs) is of key relevance for a better understanding of the physical rationale behind the recent climate changes over South Tropical South America (STSA) and their expected changes in a global warming scenario. During the last four decades, STSA experienced a lengthening of the dry season associated with diverse forcings. In this work, a set of 16 GCMs historical Coupled Model Intercomparison Project Phase 6 coupled simulations were evaluated during 1979–2014 in terms of how well they reproduced the atmospheric circulation over STSA through a circulation‐patterns (CPs) approach. Nine CPs were first identified based on low‐level winds from the ERA5 reanalysis. Focus was put on the representation of CPs during the dry‐to‐wet transition season (July‐October). Model performance depended on the seasonal cycle and spatial structure of the CPs. GCMs adequately reproduced the different CPs, with lower skills in the transition seasons. GCMs tended to go from dry to wet conditions too quickly, evidencing deficiencies in the representation of the SAMS onset, related to a poor representation of the southerly wind intrusions to STSA and the variability of the South American low‐level jet. Some GCMs were able to associate the occurrence of anomalous dry and wet years with specific CPs, suggesting well‐represented physical mechanisms controlling precipitation variability. This study could identify a few GCMs that adequately simulated the CPs in STSA (among them, CESM2, CMCC‐CM2‐HR4 and MPI‐ESM1‐2‐HR), which is relevant for driving high‐resolution models and the analysis of future projections. Key Points: Some general circulation models (GCMs) adequately represented both the seasonal cycle and spatial structure of circulation patternsGCMs often struggled in representing the monsoon initiation and the variability of the South American low‐level jetGCMs tended to go from dry to wet conditions too quickly [ABSTRACT FROM AUTHOR]

Quasi‐biennial oscillations (QBOs) in thirteen atmospheric general circulation models forced with both observed and annually repeating sea surface temperatures (SSTs) are evaluated. In most models the QBO period is close to, but shorter than, the observed period of 28 months. Amplitudes are within ±20% of the observed QBO amplitude at 10 hPa, but typically about half of that observed at lower altitudes (50 and 70 hPa). For almost all models, the oscillation's amplitude profile shows an overall upward shift compared to reanalysis and its meridional extent is too narrow. Asymmetry in the duration of eastward and westward phases is reasonably well captured, though not all models replicate the observed slowing of the descending westward shear. Westward phases are generally too weak, and most models have an eastward time mean wind bias throughout the depth of the QBO. The intercycle period variability is realistic and in some models is enhanced in the experiment with observed SSTs compared to the experiment with repeated annual cycle SSTs. Mean periods are also sensitive to this difference between SSTs, but only when parametrized non‐orographic gravity wave (NOGW) sources are coupled to tropospheric parameters and not prescribed with a fixed value. Overall, however, modelled QBOs are very similar whether or not the prescribed SSTs vary interannually. A portrait of the overall ensemble performance is provided by a normalized grading of QBO metrics. To simulate a QBO, all but one model used parametrized NOGWs, which provided the majority of the total wave forcing at altitudes above 70 hPa in most models. Hence the representation of NOGWs either explicitly or through parametrization is still a major uncertainty underlying QBO simulation in these present‐day experiments. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a key component of the global hydrological cycle, which is strongly modulated by the occurrence of different rainfall intensities. Global climate models (GCMs) commonly suffer from "too much light rain" and a negative bias in the ratio of transpiration (T) to ET (T/ET). It is unclear whether these biases are related. Here we show that with the improved simulation of probability density functions of rainfall intensity by suppressing light‐rain occurrence using a stochastic convection parameterization in the NCAR CESM1.2, the canopy T increases in tropical forests while evaporation from canopy interception and bare soil decreases. The simulated T/ET is increased by 2.5% globally and up to 8% regionally, primarily attributable to reduced fraction of wet leaves due to less frequent light rain despite its weak intensity. These results imply that excessive light rain is an important cause of the negative T/ET bias in GCMs. Plain Language Summary: Water moves from the land surface to the atmosphere via evapotranspiration. Processes contributing to evapotranspiration (ET) include evaporation from soil and wet leaves, and transpiration through pores in plants (T). Rainfall intensity is known to be an important factor in regulating canopy interception and soil moisture, thus impacting ET. "Too much light rain and too little heavy rain" and underestimated ratio of T to ET (T/ET) are two common weaknesses of current global climate models (GCMs). Here we examine the impact of rainfall intensity on climatological ET and show that light rain has a major influence on ET and its components. By improving the representation of convection, the light rain (1–20 mm d−1) frequency is reduced. As a result, the fractional coverage of wet leaves of vegetation decreases, resulting in an increase in T and a decrease in evaporation from canopy interception and bare soil. Therefore, in GCMs, the issue of excessive light rain is a cause of the problem of underestimated T/ET. Key Points: The underestimated ratio of transpiration (T) to evapotranspiration (ET) can be largely attributed to too frequent light rainThe improved rainfall intensity spectrum greatly impacts evapotranspiration by directly reducing wet leaf fraction and canopy evaporationThe associated changes in solar radiation and other factors also affect evapotranspiration by increasing vegetation transpiration [ABSTRACT FROM AUTHOR]

Even if the thresholds set out in the Paris Agreement are reached, several impacts on the climate system are still inevitable, generating new vulnerabilities and amplifying those that already exist. In this sense, the objective of this work was to analyse the impact of a global warming of 1.5 and 2°C in the regional climatological patterns of the near‐surface air temperature and precipitation over South America, locating the most affected regions and briefly discussing the possible impacts to be faced on biodiversity and agriculture. The simulations and projections of 26 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 were used, forced in four different Representative Concentration Pathways (RCPs) scenarios. Projections for 1.5 or 2°C global warming indicate a local increase even higher, of at least +0.5°C, in almost the entire South American continent. Regarding precipitation, a similar pattern was also found between the two thresholds of global warming. GCMs project an increase of about 100 mm year−1 in the southern region of Brazil and in the northern portion of the Brazilian Northeast, in northern Argentina, Uruguay, and parts of Peru, Ecuador, Colombia and Venezuela; while the areas between the south of Chile and Argentina and the extreme north of South America show reductions of up to 150 mm year−1. In addition, both the temperature and precipitation patterns were practically similar between the four analysed RCPs scenarios. The results presented in this study indicate that even if the Paris Agreement is very successful, South America will still suffer several impacts and will need to take effective adaptation measures in the short term. This may have pervasive implications for the biodiversity and genetic resource base of the subcontinent, as well as may impair agricultural productivity or incur into considerable adaptation costs for the sector. [ABSTRACT FROM AUTHOR]

The effectiveness of bias correction (BC) of global‐scale future climate projections is crucial in climate change studies. The magnitude of error in the BC affect climate change adaptation decisions. The existing BC methods vary in their complexity, and exhibit limitations on data length, degrees of freedom etc. This study proposes a new method, L‐moments Scaling (L‐mS), which is both parsimonious and efficient in bias‐correcting extreme rainfall events. The L‐mS method applies corrections to the first three L‐moments of the data to bias correct the entire distribution. The proposed method's efficiency was demonstrated at two stations in India, Chennai and Hyderabad, for 1‐day Annual Maximum (AM) precipitation simulations from EC‐EARTH and MIROC5 models. A comparison was performed with five widely used BC methods using two validation procedures: Strict Split‐Sample (SSS) and Bootstrapped Split‐Sample (BSS). The results revealed that the L‐mS method could outperform all the five BC Methods with increased accuracy (0%–18% in SSS and 3%–21% in BSS), and with minimal variability among the bootstrapped samples in terms of Normalized‐Root‐Mean‐Square‐Error (NRMSE). The method was also applied on 1° gridded data over India for 1‐day, 2‐day, 3‐day, 7‐day AM, and Annual Totals, and the future (2021–2050) projections were bias‐corrected using L‐mS method. The L‐mS method produced at least 2.5 and 3 times lesser error in mean and standard deviation, respectively, compared to observed extremes, across all the grids. The L‐mS method was able to utilize the inherent nature of frequency domain analysis to outperform similar advanced methods by correcting the entire AM data with few key statistics and could serve as an efficient tool in the BC of extreme climate variables. Also, the bias‐corrected future projections indicated the magnitudes of extreme rainfall events are expected to decrease in 35%–40% and increase in 60%–65% of the grids. [ABSTRACT FROM AUTHOR]

Domestic energy consumption in the United Kingdom depends on both meteorological and socio‐economic factors. The former are dominated by the effect of temperature during the colder months of the year, with the energy demand increasing as the temperature decreases. Warming of the UK climate under the influence of anthropogenic forcings is therefore expected to lead to a reduction in domestic energy consumption. Here, we present an end‐to‐end attribution study that investigates whether the anthropogenic effect on consumption is already evident in the United Kingdom. We analyse data of gas and electricity use in UK households during 2008–2019 and use a simple linear model to express the temperature dependence. Uncertainties in the resulting transfer functions are derived with a recent methodology, originally introduced for downscaling purposes, but adapted here for use in impact studies. The transfer functions are applied to temperature data from simulations with and without the effect of human influence on the climate, generated by 11 state‐of‐the‐art climate models. We thus assess the anthropogenic impact on energy consumption during the reference period by comparing it with what it might have been in a climate without anthropogenic climate change, but at the same level of adaptation. We find that without human influence on the climate, UK households would consume on average about 1,400 kWh more per year, which would increase the annual energy bills by about 70 GBP. Our attribution assessment provides useful evidence of an impact that has already emerged, which can help inform UK's adaptation plans as the climate continues to warm. [ABSTRACT FROM AUTHOR]

Downscaling is widely used in studies of local and/or regional climate as it yields a greater spatial resolution than general circulation models (GCMs) can provide. It utilizes GCM output or reanalysis data, which is transformed using mathematical relationships or used to force the lateral boundaries of a regional climate model. However, there is no set selection technique to determine which GCM realization(s) to employ. Here, a comprehensive yet easily applicable model selection technique for studies requiring GCM data as a constraint was developed. The technique evaluates, with respect to a reanalysis product and/or observational data, the ability of GCM realizations to reconstruct the mean state of the climate and the space‐time climatic anomalies for the atmospheric state variables at three distinct pressure levels. It was applied to the region of East Africa, where GISS‐E2‐H r6i1p3 was found to perform the strongest. The top ranked realizations were found to better capture processes when evaluated for the example of the Indian Ocean Dipole. Furthermore, the surface air temperature and precipitation from three 10‐year regional climate model simulations, one forced by the Modern‐Era Retrospective Analysis for Research and Applications version 2 reanalysis, one forced by the top ranked GCM, and one by the lowest ranked one, were compared to gridded observations. Results show that using a top ranked GCM for the boundary conditions leads to a better dynamical downscaling simulation than a low‐ranked GCM, suggesting the potential of the proposed technique for future downscaling techniques. Key Points: Creation of a comprehensive and easily applicable model selection technique for downscalingTop ranked models are better able to capture processes for Indian Ocean Dipole exampleUsing a top ranked general circulation model (GCM) for the boundary conditions leads to a better dynamical downscaling simulation than a low‐ranked GCM [ABSTRACT FROM AUTHOR]

Maize production on the North China Plain (NCP) is critical to food security in China; however, currently, it is affected by climate change. Understanding the spatiotemporal distribution of the climate suitability for maize on the NCP in the present and future may help sustainably use climate‐related resources to ensure food security in China. In this study, 30 general circulation models from the Coupled Model Intercomparison Project Phase 5 and a statistical downscaling model (NWAI‐WG) were used to project meteorological data in 2021–2100 under representative concentration pathway 4.5 (RCP4.5) and RCP8.5 for 23 national climatic stations on the NCP. Based on agricultural climate suitability theory and the fuzzy mathematics method, the suitability of temperature, precipitation, and solar radiation on summer maize were analysed. The results showed that temperature suitability is decreasing for 2021–2100, especially during the stages from jointing to maturity; temperature suitability is the lowest in the southwest and increases to the northeast under both scenarios. Compared to 1996–2015, the precipitation suitability in 2021–2100 increases greatly under both scenarios, especially in the central part under RCP4.5 and in the north part under RCP8.5. Solar radiation suitability shows a decreasing trend for 1996–2015, however, an increasing trend for 2021–2100 under both scenarios. At spatial scale, the solar suitability increases from southwest to northeast. The integrated climate suitability under RCP4.5 in 2021–2100 is averaged approximately 0.8 and varies slightly, indicating climate change may do small effect on maize growth, though the high values shifting from the central part in 2021–2040 to the northern part in 2081–2100; however, under RCP8.5, the integrated climate suitability shows a downward trend, indicating that climate change will make many regions less suitable for maize growth. These results could provide basic information for agriculture to adapt to climate change and ensure food security for China. [ABSTRACT FROM AUTHOR]

This study examines the role of the mean state in the propagation of the Madden‐Julian oscillation (MJO) over the Maritime Continent (MC). We use an ensemble of simulations made with a single model—the Community Earth System Model version 2—to assess the effect of the mean state that is unaffected by that of model components such as parameterization schemes. Results show that the background meridional moisture gradient is much steeper over the MC region in the periods with a stronger MJO propagation. Column water vapor budget of the MJO strongly suggests that the simulated mean state affects MJO via its impacts on moisture dynamics—a greater advection of mean moisture by MJO wind in the MC region is responsible for the anomalous MJO activity. Plain Language Summary: The Madden‐Julian oscillation (MJO) is a planetary‐scale, eastward moving envelope of anomalous convection in the tropics. It is the dominant mode of sub‐seasonal variability in the tropics. Unfortunately, an accurate representation of the MJO has historically been a challenging task for many, if not most, global climate models. The mean state distribution of atmospheric moisture has been highlighted as a key aspect affecting the simulation of MJO propagation in many recent modeling studies. When many different models are compared, however, it is difficult to isolate the role of the mean state because different models use different parameterizations of moist physics that affect both the mean state and the MJO directly. In this study, we examine the relationship between the mean state and MJO propagation in an ensemble of simulations made with a single coupled model—the Community Earth System Model version 2 (CESM2). Each ensemble member differs only in its initial conditions and thus the parameterizations and resolution are identical. We found that MJO propagation over the MC in CESM2 is strongly affected by the background meridional moisture gradient (MMG), with MJO propagation being enhanced in the periods with a steeper MMG. Key Points: Each ensemble in the Community Earth System Model version 2 historical simulation shows a marked low‐frequency variability in Madden‐Julian oscillation (MJO) propagation across the Maritime Continent (MC)Simulation periods with enhanced MJO propagation across the MC exhibit a steeper background meridional moisture gradient (MMG) around the MCThe steeper background MMG strengthens moisture recharging to the east of MJO, leading its eastward propagation [ABSTRACT FROM AUTHOR]

Depositional stratigraphy represents the only physical archive of palaeo‐sediment routing and this limits analysis of ancient source‐to‐sink systems in both space and time. Here, we use palaeo‐digital elevation models (palaeoDEMs; based on high‐resolution palaeogeographic reconstructions), HadCM3L general circulation model climate data and the BQART suspended sediment discharge model to demonstrate a predictive, forward approach to palaeo‐sediment routing system analysis. To exemplify our approach, we use palaeoDEMs and HadCM3L data to predict the configurations, geometries and climates of large continental catchments in the Cenomanian and Turonian North American continent. Then, we use BQART to estimate suspended sediment discharges and catchment‐averaged erosion rates and we map their spatial distributions. We validate our estimates with published geologic constraints from the Cenomanian Dunvegan Formation, Alberta, Canada, and the Turonian Ferron Sandstone, Utah, USA, and find that estimates are consistent or within a factor of two to three. We then evaluate the univariate and multivariate sensitivity of our estimates to a range of uncertainty margins on palaeogeographic and palaeoclimatic boundary conditions; large uncertainty margins (≤50%/±5°C) still recover estimates of suspended sediment discharge within an order of magnitude of published constraints. PalaeoDEMs are therefore suitable as a first‐order investigative tool in palaeo‐sediment routing system analysis and are particularly useful where stratigraphic records are incomplete. We highlight the potential of this approach to predict the global spatio‐temporal response of suspended sediment discharges and catchment‐averaged erosion rates to long‐period tectonic and climatic forcing in the geologic past. [ABSTRACT FROM AUTHOR]

Socioeconomic drought occurs when water supply from a regional water resources system cannot meet the water demands. Even if a socioeconomic drought ends, the antecedent water deficit may continue to have impacts for some time, thus influencing the resilience of a regional water resources system. To take this into account, especially under growing population and changing climate, this study develops a new method through integrating a new index, Water Resources System Resilience Index (WRSRI), into socioeconomic drought event identification. The new index represents the percentage of the antecedent water deficit for a socioeconomic drought that can be recovered from the excess water during subsequent periods through analyzing records of a historical drought event. The methodology, implemented on the East River Basin in South China, involves three major steps: (1) calculation of WRSRI value; (2) analysis of key features of identified future socioeconomic drought events, that is, total number, longest duration (LD), and percentage on different drought levels; and (3) sensitivity analysis of WRSRI based on a total of 52 streamflow data sets generated from General Circulation Model outputs and using a macroscale hydrologic model. The results indicate that, for each data set, the total number decreases with an increase in WRSRI but the LD increases; moreover, for most data sets, the LD is less sensitive within the WRSRI range of [0, 0.6]. The outcomes of this study can enhance our capability in more effectively assessing the resilience of a regional water resources system, especially under changing future socioeconomic and environmental conditions. Key Points: A new index considering resilience of a regional water resources system to socioeconomic droughts under changing conditions is proposedResilience of the East River Basin in China, which represents the recoverable water deficit, is determined based on historical dataKey features of future socioeconomic drought events identified are studied, and their sensitivities to resilience are analyzed [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the historical skill of models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in two regions of Ethiopia: northwestern Ethiopia and the Awash, one of the main Ethiopian river basins. An ensemble of CMIP5 models was first selected so that atmosphere‐only (Atmospheric Model Intercomparison Project, AMIP) and fully coupled simulations could be directly compared, assessing the effects of coupled model sea surface temperature (SST) biases. The annual cycle, seasonal biases, trends, and variability were used as metrics of model skill. In the Awash basin, both coupled and AMIP simulations had late Belg or March‐May (MAM) rainy seasons. In connection to this, most models also missed the June rainfall minimum entirely. Northwest Ethiopia, which has a unimodal rainfall cycle in observations, is shown to have bimodal seasonality in models, even in the AMIP simulations. Significant AMIP biases in these regions show that model biases are not related to SST biases alone. Similarly, a clear connection between model resolution and skill was not found. Models simulated temperature with more skill than rainfall, but trends showed an underestimation in Belg (MAM/April‐May (AM)) trends, and an overestimation in Kiremt or July‐September (JAS/June‐September (JJAS)) trends. The models which were shown to have the most skill in a range of categories were HadGEM2‐AO, GFDL‐CM3, and MPI‐ESM‐MR. The biases and discrepancies in model skill for different metrics of rainfall and temperature found in this study provide a useful basis for a process‐based analysis of the CMIP5 ensemble in Ethiopia. [ABSTRACT FROM AUTHOR]

Climate change research often relies on downscaled general circulation models (GCM), projections of future scenarios that are used to build ecological and evolutionary models. With more than 35 different GCMs widely available at a resolution of 10 km and finer, standardized methods to understand the differences among GCM projections in a region of interest and to choose which GCM to use for analysis are essential to maximize relevance to policy and to assure a proper treatment of uncertainty.To help researchers and policymakers understand and select form the range of available GCM scenarios, we have developed GCM compareR, an open‐source web application written in r using shiny. GCM compareR is freely accessible with an easy interactive user interface, has preloaded climate scenario data to increase the speed of analysis and is fully documented to ensure reproducibility. Users of the application need no prior experience in coding.GCM compareR is designed to compare GCMs and different climate change scenarios to provide full, documented exploration of the possible alternative futures from within the range of projections in CMIP5 climate models. Designed with a wide group of users in mind, including ecologists, conservationists and policymakers, the application is designed to adapt analyses to any geographic area of interest. Results are provided as figures, tables and maps that clearly communicate the differences among model projections for the region. Additionally, the tool allows for the export of a report that records the parameter choices and results of a session, along with contextual information, to make the analysis fully transparent and replicable. [ABSTRACT FROM AUTHOR]

The climate and circulation of a terrestrial planet are governed by, among other things, the distance to its host star, its size, rotation rate, obliquity, atmospheric composition and gravity. Here we explore the effects of the last of these, the Newtonian gravitational acceleration, on its atmosphere and climate. We first demonstrate that, if the atmosphere obeys the hydrostatic primitive equations, which are a very good approximation for most terrestrial atmospheres, and if the radiative forcing is unaltered, changes in gravity have no effect at all on the circulation except for a vertical rescaling. That is to say, the effects of gravity may be completely scaled away and the circulation is unaltered. However, if the atmosphere contains a dilute condensible that is radiatively active, such as water or methane, then an increase in gravity will generally lead to a cooling of the planet because the total path length of the condensible will be reduced as gravity increases, leading to a reduction in the greenhouse effect. Furthermore, the specific humidity will decrease, leading to changes in the moist adiabatic lapse rate, in the Equator‐to‐Pole heat transport, and in the surface energy balance because of changes in the sensible and latent fluxes. These effects are all demonstrated both by theoretical arguments and by numerical simulations with moist and dry general circulation models. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are implemented in Super‐Parameterized Energy Exascale Earth System Model (SP‐E3SM) to imitate the shortwave and longwave radiative transfer calculations. These DNNs were able to emulate the radiation parameters with an accuracy of 90–95% at a cost of 8–10 times cheaper than the original radiation parameterization. A comparison of time‐averaged radiative fluxes and the prognostic variables manifested qualitative and quantitative similarity between the DNN emulation and the original parameterization. It has also been found that the differences between the DNN emulation and the original parameterization are comparable to the internal variability of the original parameterization. Although the DNNs developed in this investigation emulate the radiation parameters for a specific set of initial conditions, the results justify the need of further research to generalize the use of DNNs for the emulations of full model radiation and other parameterization for seasonal predictions and climate simulations. Plain Language Summary: The radiative transfer calculations in general circulation models often impose a computational challenge owing to the complexity of the current radiation models. In this study, we have demonstrated that deep neural networks can reduce the cost of radiative transfer calculations in the Super‐Parameterized Energy Exascale Earth System Model while maintaining accuracy. Key Points: We build deep neural networks (DNNs) to emulate the shortwave and longwave radiation parameterization in Super‐Parameterized E3SMThe DNNs accurately predict the instantaneous and annually averaged radiative fluxesThe DNNs compute the radiative fluxes 8‐10 times faster than the original radiation parameterization [ABSTRACT FROM AUTHOR]

The Colorado River basin (CRB) is the primary source of water in the southwestern United States. A key step to reduce the uncertainty of future streamflow projections in the CRB is to evaluate the performance of historical simulations of general circulation models (GCMs). In this study, this challenge is addressed by evaluating the ability of 19 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and four nested regional climate models (RCMs) in reproducing the statistical properties of the hydrologic cycle and temperature in the CRB. To capture the transition from snow‐dominated to semi‐arid regions, analyses are conducted by spatially averaging the climate variables in four nested sub‐basins. Most models overestimate the mean annual precipitation (P) and underestimate the mean annual temperature (T) at all locations (up to +140% and −4.9 °C, respectively). A group of models capture the mean annual run‐off at all sub‐basins with different strengths of the hydrological cycle, depending on the level of P overestimation. Another set of models overestimate the mean annual run‐off, due to a weak cycle in the evaporation channel. An abrupt increase in the mean annual T of ~0.8 °C is detected at all locations around 1980 from the observed and most of the simulated time series. However, no statistically significant monotonic trends emerge for both P and T. All models simulate the seasonality of T quite well. The phasing of the seasonal cycle of P is reproduced fairly well in one of the upper, snow‐dominated sub‐basins. Model performances degrade in the larger sub‐basins that include semi‐arid areas, because several GCMs are not able to capture the effect of the North American monsoon. Finally, the relative performances of the climate models in reproducing the climatologies of P and T are quantified to support future impact studies in the basin. [ABSTRACT FROM AUTHOR]

ABSTRACT: The fragile ecosystem, scarce water resources, and limited ecosystem resilience of Xinjiang, China make the region especially vulnerable to climate change. Researchers need reliable analyses of climate change trends to formulate regional mitigation and adaption strategies and to support sustainable development. Therefore, two statistical downscaling methods and the combination of a Mann–Kendall test with Co‐Kriging interpolation were used to investigate the spatiotemporal characteristics of climate change in Xinjiang based on the ensemble of 37 general circulation models (GCMs) in 2021–2060. The reliabilities of single run and ensemble downscaling results of GCMs were evaluated by 69 meteorological stations over the period 1965–2004. The correlation coefficients (CC) with the observations for precipitation and temperature ranged between 0.4–0.8 and 0.9–0.99 in the reference period, respectively. CC improved to 0.87 and was close to 1 after downscaling for precipitation and temperature, respectively. A pronounced increase of ca. 0.27–0.51 °C decade−1 was projected overall, and especially for Representative Concentration Pathway (RCP) 8.5 in northern Xinjiang. In general, the precipitation changed by −1.66 to 6.83% decade−1 while varying seasonally and spatially; a declining tendency emerged in the western regions of Xinjiang during summer. More extreme rainfall events are predicted to occur in summer and autumn months, while warmer extremes would be concentrated in August. The climate in Xinjiang will continue to become warmer and wetter. Nevertheless, Western Xinjiang will experience a warmer and drier climate in summer and autumn. These projections of climate change in the near future are able to provide useful information for the development of potential mitigation measures and adaptation strategies. Multiple circulation mechanisms are also suggested accounting for the changes in precipitation and temperature which will benefit the understanding of the possible drivers of climate variability in this region. [ABSTRACT FROM AUTHOR]

Abstract: Climate change represents a primary threat to species persistence and biodiversity at a global scale. Cold adapted alpine species are especially sensitive to climate change and can offer key “early warning signs” about deleterious effects of predicted change. Among mountain ungulates, survival, a key determinant of demographic performance, may be influenced by future climate in complex, and possibly opposing ways. Demographic data collected from 447 mountain goats in 10 coastal Alaska, USA, populations over a 37‐year time span indicated that survival is highest during low snowfall winters and cool summers. However, general circulation models (GCMs) predict future increase in summer temperature and decline in winter snowfall. To disentangle how these opposing climate‐driven effects influence mountain goat populations, we developed an age‐structured population model to project mountain goat population trajectories for 10 different GCM/emissions scenarios relevant for coastal Alaska. Projected increases in summer temperature had stronger negative effects on population trajectories than the positive demographic effects of reduced winter snowfall. In 5 of the 10 GCM/representative concentration pathway (RCP) scenarios, the net effect of projected climate change was extinction over a 70‐year time window (2015–2085); smaller initial populations were more likely to go extinct faster than larger populations. Using a resource selection modeling approach, we determined that distributional shifts to higher elevation (i.e., “thermoneutral”) summer range was unlikely to be a viable behavioral adaptation strategy; due to the conical shape of mountains, summer range was expected to decline by 17%–86% for 7 of the 10 GCM/RCP scenarios. Projected declines of mountain goat populations are driven by climate‐linked bottom‐up mechanisms and may have wide ranging implications for alpine ecosystems. These analyses elucidate how projected climate change can negatively alter population dynamics of a sentinel alpine species and provide insight into how demographic modeling can be used to assess risk to species persistence. [ABSTRACT FROM AUTHOR]

ABSTRACT A probabilistic framework for evaluating changes in offshore wind potential under a changing climate is presented considering three general circulation models ( GCMs), two representative concentration pathways and two statistical downscaling techniques. The analysis was done with respect to three specific locations along the Indian coastline, identified earlier for future offshore energy extraction. The uncertainty among the GCMs was addressed and the choice of an appropriate GCM was made by combining analysis of GCM uncertainty and model reliability. The regional scale wind was derived from the selected GCM using one hard computing method, namely bilinear interpolation and quantile mapping, and one soft method, an artificial neural network. The efficiency of these methods was assessed using certain performance criteria for the historical period and a convergence criterion over the future period. To evaluate wind potential, the GCM output was downscaled from daily to hourly, taking into account the removal of gusts because of long interval averaging. The wind potential for past (1979-2005) and future (2006-2032) time slices was determined using both hard and soft methods as well as two representative concentration pathways. The resulting four outcomes at annual and seasonal scale were compared with the extrapolated trend of past wind potential derived from both GCM and reanalysis data. Finally, the most efficient strategy was evaluated and the actual extractable power was estimated considering a standard power curve. It is concluded that at all the three locations the annual average wind potential will substantially increase, benefitting the offshore industry. [ABSTRACT FROM AUTHOR]

Species distribution models ( SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross-validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland-dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross-validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross-validation results were correlated with extrapolation results, the use of cross-validation performance metrics to guide modeling choices where knowledge is limited was supported. [ABSTRACT FROM AUTHOR]

ABSTRACT: Climate change has and will continue to impact the magnitude and timing of streamflow throughout the world. While streamflow timing of western United States watersheds has shifted earlier in mountainous snow‐dominated watersheds due to earlier snowmelt, timing for rain‐dominated coastal watersheds has shifted to later in the year. Despite these dissimilarities, coastal watersheds have received little attention in the literature. This research assesses changes in projected climate and hydrology for five small coastal basins (42–718 km2) in Washington, Oregon, and California. Projections of temperature and precipitation from General Circulation Models (GCMs) for Representative Concentration Pathways 4.5 and 8.5 are coupled with the Soil and Water Assessment Tool hydrologic model to simulate future hydrology for each watershed. The response of climate, streamflow discharge, and timing was analysed for each watershed by comparing the projected mid‐21st century (2035–2064) and late‐21st century (2070–2099) to the historical period (1970–1999). While temperature is projected to increase at each watershed from 1 to 6 °C by the end of the 21st century, precipitation varies widely with the median and mean change across all watersheds and GCMs being close to zero (just 2.0 and 3.6%, respectively). Changes in the magnitude of peak winter streamflow discharge differ across the region compared to the historical period. Streamflow timing also features latitudinal divergence, with the southern California watershed projecting later timing by 13 days and all other watersheds indicating smaller shifts (0–7 days) for the end of the 21st century compared to the historical time period, largely from corresponding shifts in precipitation timing. The projected decreases in peak winter streamflow magnitude, particularly in central and southern California, have critical implications for water supply, which is already strained from extensive drought and high demand. This research makes strides to assess new trends across coastal watersheds. [ABSTRACT FROM AUTHOR]

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Anthropogenic climate change is already impacting ecological systems. Understanding how organisms respond to weather (short-term) and climate (long-term) variability, and the population and ecosystem-wide consequences of climate change, is a research priority. The appropriate use of information on past and potential future weather and climate conditions is crucial for such research, but uncertainties and biases in this information are seldom given full consideration, with important consequences for assessing the potential impacts of climate change on the conservation of biodiversity. Here, we highlight three important neglected issues pertaining to the major applications of weather and climate information in ecological and biogeographical studies. These are as follows: (1) the uncertainty associated with historical weather and climate information; (2) the selection of ensembles of simulated future climate conditions derived from general circulation models ( GCM); and (3) the uncertainty and assumptions associated with downscaling GCM simulations to ecologically relevant spatial scales. Broadly, in order to improve the use of weather and climate information in ecological studies, we propose that ecologists must: (1) use weather and climate products that are appropriate for their intended purpose; (2) explore the consequences of uncertainty in these products for ecological conclusions; and (3) seek greater integration of ecological and climate research to create products that reflect both the requirements of ecologists and the limits of climatology. This will enable more effective research into the likely responses of ecological systems to future climate change. [ABSTRACT FROM AUTHOR]

ABSTRACT Developing high-quality long-term data sets at uniform space-time resolution is essential for improved climate studies. This article processes the outputs from two global and regional climate models, the Community Climate System Model ( CCSM3) and the Regional Climate Model driven by the Hadley Centre Coupled Model ( RegCM3). The results are bias-corrected time series of atmospheric variables corresponding to Intergovernmental Panel on Climate Change ( IPCC's) historical ( 20C3M) and future ( A2) climate scenarios over the Amazon Basin. We use a series of simple but effective interpolation approaches to produce hourly climate data sets at 1° by 1° grid cells. A quantile-based mapping approach is used to reduce the biases of temperature and precipitation in CCSM3 and RegCM3. Adjustments are also made on specific humidity and downwelling longwave radiation to avoid inconsistency between those variables and bias-corrected temperature values. We also interpolated an already bias-corrected Parallel Climate Model data set ( PCM1) from 3-hourly to the hourly resolution. The final climate data sets can be used as forcing of ecosystem and hydrologic models to study climate changes and impact assessments over the Amazon Basin. [ABSTRACT FROM AUTHOR]

Given the threats of climate change, understanding the relationship of climate with long-term population dynamics is critical for wildlife conservation. Previous studies have linked decadal climate oscillations to indices of juvenile recruitment in loggerhead sea turtles ( Caretta caretta), but without a clear understanding of mechanisms. Here, we explore the underlying processes that may explain these relationships. Using the eddy-resolving Ocean General Circulation Model for the Earth Simulator, we generate hatch-year trajectories for loggerhead turtles emanating from Japan over six decades (1950-2010). We find that the proximity of the high-velocity Kuroshio Current to the primary nesting areas in southern Japan is remarkably stable and that hatchling dispersal to oceanic habitats itself does not vary on decadal timescales. However, we observe a shift in latitudes of trajectories, consistent with the Pacific Decadal Oscillation ( PDO). In a negative PDO phase, the Kuroshio Extension Current ( KEC) is strong and acts as a physical barrier to the northward transport of neonates. As a result, hatch-year trajectories remain mostly below 35°N in the warm, unproductive region south of the Transition Zone Chlorophyll Front ( TZCF). During a positive PDO phase, however, the KEC weakens facilitating the neonates to swim north of the TZCF into cooler and more productive waters. As a result, annual cohorts from negative PDO years may face a lack of resources, whereas cohorts from positive PDO years may find sufficient resources during their pivotal first year. These model outputs indicate that the ocean circulation dynamics, combined with navigational swimming behavior, may be a key factor in the observed decadal variability of sea turtle populations. [ABSTRACT FROM AUTHOR]

Paleoclimate data assimilation has recently emerged as a promising technique to estimate past climate states. Here we test two of the underlying assumptions of paleoclimate data assimilation as applied so far: (1) climate proxies can be modeled as linear, univariate recorders of temperature and (2) structural errors in GCMs can be neglected. To investigate these two points and related uncertainties, we perform a series of synthetic, paleoclimate data assimilation-based reconstructions where 'pseudo' proxies are generated with physically based proxy system models (PSMs) for coral δ18 O, tree ring width, and ice core δ18 O using two isotope-enabled atmospheric general circulation models. For (1), we find that linear-univariate models efficiently capture the GCM's climate in ice cores and corals and do not lead to large losses in reconstruction skill. However, this does not hold for tree ring width, especially in regions where the trees' response is dominated by moisture supply; we quantify how the breakdown of this assumption lowers reconstruction skill for each proxy class. For (2), we find that climate model biases can introduce errors that greatly reduce reconstruction skill, with or without perfect proxy system models. We explore possible strategies for mitigating structural modeling errors in GCMs and discuss implications for paleoclimate reanalyses. [ABSTRACT FROM AUTHOR]

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species-climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models ( GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models ( SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040-2069, 2070-2099), using downscaled climate projections, and calculated species turnover and changes in species-specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species-specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site-level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses. [ABSTRACT FROM AUTHOR]

Many tropical islands have limited water resources with historically increasing demand, all potentially affected by a changing climate. The effects of climate change on island hydrology are difficult to model due to steep local precipitation gradients and sparse data. This work uses 10 statistically downscaled general circulation models (GCMs) under two greenhouse gas emission scenarios to evaluate the uncertainty propagated from GCMs in projecting the effects of climate change on water resources in a tropical island system. The assessment is conducted using a previously configured hydrologic model, the Precipitation Runoff Modelling System (PRMS) for Puerto Rico. Projected climate data and their modelled hydrologic variables versus historical measurements and their modelled hydrologic variables are found to have empirical distribution functions that are statistically different with less than 1 year of daily data aggregation. Thus, only annual averages of the projected hydrologic variables are employed as completely bias-corrected model outputs. The magnitude of the projected total low decreases in the four regions covering Puerto Rico, but with a large range of uncertainty depending on the makeup of the GCM ensemble. The multi-model mean projected total low decreases by 49-88%of historical amounts from the 1960s to the 2090s for the high emissions scenarios and by 39-79% for the low emissions scenarios. Subsurface low contributions decreased the least and groundwater low contributions decreased the most across the island. At locations critical to water supply for human use, projected stream low is shown to decrease substantially below projected withdrawals by 2099. [ABSTRACT FROM AUTHOR]

The typical short generation length of insects makes their population dynamics highly sensitive not only to mean annual temperatures but also to their intra-annual variations. To consider the combined effect of both thermal factors under global warming, we propose a modeling framework that links general circulation models (GCMs) with a stochastic weather generator and population dynamics models to predict species population responses to inter- and intra-annual temperature changes. This framework was utilized to explore future changes in populations of Bemisia tabaci, an invasive insect pest-species that affects multiple agricultural systems in the Mediterranean region. We considered three locations representing different pest status and climatic conditions: Montpellier (France), Seville (Spain), and Beit-Jamal (Israel). We produced ensembles of local daily temperature realizations representing current and future (mid-21st century) climatic conditions under two emission scenarios for the three locations. Our simulations predicted a significant increase in the average number of annual generations and in population size, and a significant lengthening of the growing season in all three locations. A negative effect was found only in Seville for the summer season, where future temperatures lead to a reduction in population size. High variability in population size was observed between years with similar annual mean temperatures, suggesting a strong effect of intra-annual temperature variation. Critical periods were from late spring to late summer in Montpellier and from late winter to early summer in Seville and Beit-Jamal. Although our analysis suggested that earlier seasonal activity does not necessarily lead to increased populations load unless an additional generation is produced, it is highly likely that the insect will become a significant pest of open-fields at Mediterranean latitudes above 40° during the next 50 years. Our simulations also implied that current predictions based on mean temperature anomalies are relatively conservative and it is better to apply stochastic tools to resolve complex responses to climate change while taking natural variability into account. In summary, we propose a modeling framework capable of determining distinct intra-annual temperature patterns leading to large or small population sizes, for pest risk assessment and management planning of both natural and agricultural ecosystems. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, statistical downscaling of general circulation model ( GCM) simulations to monthly inflows of Kemer Dam in Turkey under A1B, A2, and B1 emission scenarios has been performed using machine learning methods, multi-model ensemble and bias correction approaches. Principal component analysis ( PCA) has been used to reduce the dimension of potential predictors of National Centers for Environmental Prediction and National Center for Atmospheric Research ( NCEP/ NCAR) reanalysis data. Then, the reasonable GCMs were selected by investigating the rank correlations between the selected predictors in NCEP/ NCAR reanalysis data and those in GCMs for 20C3M scenario between periods 1979 and 1999. Upon the training of feedforward neural network ( FFNN), least squares support vector machine ( LSSVM) and relevance vector machine ( RVM) downscaling models, the general performance of the downscaled predictions using NCEP/ NCAR reanalysis data for Kemer watershed showed that the trained RVM model produced adequate results. The effectiveness of RVM model was illustrated by its integration with 20C3M scenario between periods 1979 and 1999 and A1B, A2, and B1 future climate scenarios between periods 2010 and 2039. Afterwards, the flow forecasts were obtained by building a multi-model ensemble through the selected GCMs followed by a bias correction approach. Finally, the significance of the probable changes in trends was identified through statistical tests based on the corrected forecasts. Results showed that decreasing flows trends in winter, spring and fall seasons have been foreseen over the study area for the period between 2010 and 2039. [ABSTRACT FROM AUTHOR]

ABSTRACT Future climate change projections are not limited to a simple warming, but changes in precipitation and sea level pressure (SLP) are also projected. The SLP changes and the associated atmospheric circulation changes could directly mitigate or enhance potential projected changes in temperature and precipitation associated with rising temperatures. With the aim of analysing the projected circulation changes and their possible impacts on temperature and precipitation over Europe in summer [June-July-August ( JJA)], we apply an automatic circulation type classification method, based on daily SLP, on general circulation model ( GCM) outputs from the Coupled Model Intercomparison Project phase 5 ( CMIP5) database over the historical period (1951-2005) and for climate under two future scenarios (2006-2100). We focus on summer as it is the season when changes in temperature and precipitation have the highest impact on human health and agriculture. Over the historical observed reference period (1960-1999), our results show that most of the GCMs have significant biases over Europe when compared to reanalysis data sets, both for simulating the observed circulation types and their frequencies, as well as for reproducing the intraclass means of the studied variables. The future projections suggest a decrease of circulation types favouring a low centred over the British Isles for the benefit of more anticyclonic conditions. These circulation changes mitigate the projected precipitation increase over north-western Europe in summer, but they do not significantly affect the projected temperature increase and the precipitation decrease over the Mediterranean region and eastern Europe. However, the circulation changes and the associated precipitation changes are tarnished by a high uncertainty among the GCM projections. [ABSTRACT FROM AUTHOR]

Six Single-Column Model (SCM) versions of climate models are evaluated on the basis of their representation of the dependence of the stratocumulus-topped boundary layer regime on the free tropospheric thermodynamic conditions. The study includes two idealized experiments corresponding to the present-day and future climate conditions in order to estimate the low-cloud feedback. Large-Eddy Simulation (LES) results are used as a benchmark and GCM outputs are included to assess whether the SCM results are representative of their 3-D counterparts. The SCMs present a variety of dependencies of the cloud regime on the free tropospheric conditions but, at the same time, several common biases. For all the SCMs the stratocumulus-topped boundary layer is too shallow, too cool, and too moist as compared to the LES results. Moreover, they present a lack of clouds and liquid water and an excess of precipitation. The disagreement among SCMs is even more distinct for the response to a climate perturbation. Even though the overall feedback is positive for all the models, in line with the LES results, the SCMs show a rather noisy behavior, which depends irregularly on the free tropospheric conditions. Finally, the comparison with the host GCM outputs demonstrates that the considered approach is promising but needs to be further generalized for the SCMs to fully capture the behavior of their 3-D counterparts. [ABSTRACT FROM AUTHOR]

ABSTRACT This study aims to analyse the skill of state-of-the-art of five general circulation models ( GCMs) in predicting winter precipitation over northern India. The precipitation in winter season (December, January and February) is very important for Rabi crops in north India, particularly for wheat, as it supplements moisture and maintains low temperature for the development of the crops. The GCM outputs (seasonal mean forecasts issued in November) from various organizations are compared with the observed high-resolution gridded rainfall data obtained from India Meteorological Department ( IMD). Prediction skill of such GCMs is examined for the period 1982-2009. The climatology, interannual standard deviation ( ISD) and correlation coefficients have been computed for the five GCMs and compared with observation. It is found that the models are able to reproduce the climatology and ISD to varying degrees; however, skill of predictions is too low. Multi-model ensemble ( MME) approaches have been employed. It is found that the weighted MME using multiple linear regression technique improves the prediction skill of winter precipitation over northern India. The teleconnection between the sea surface temperature ( SST) and winter precipitation revealed that the SST over the Pacific Ocean affects the precipitation over north India in winter season. While this observed feature is represented well by some models with high fidelity, most models are unable to respond to SST variations in the Pacific Ocean in a realistic manner. Lagged correlations between the north India rainfall and SST over the Niño-3.4 region reveal that only two of the five GCMs get the observed simultaneous teleconnection correctly. Furthermore, only one of these two models has the observed phase lag with the strongest correlation as observed. [ABSTRACT FROM AUTHOR]

ABSTRACT A cluster analysis-based synoptic classification scheme, known as the Kidson types, was applied to reanalyses and Coupled Model Inter-comparison Project phase 3 ( CMIP3) general circulation model ( GCM) output over New Zealand to identify the potential for future changes in regional circulation. Results indicate that a number of GCM 20th century control runs reproduce the type frequencies observed in reanalysis data. Application to future scenario runs for the periods 2046-2065 and 2081-2100 displays little variation in the annual frequency of the synoptic types relative to the 20th century, especially when uncertainty associated with the model ensemble is considered. This is surprising in the context of previous work on possible future movements in jet position and subsequent impacts on weather patterns. A sensitivity analysis that mimics the movement of the jet position was performed, revealing that the annual type frequencies are relatively insensitive to change. To determine whether this is a problem with the synoptic typing scheme, a correlation-based classification technique was also used, but showed similar results. This work highlights issues with applying synoptic classification schemes to GCM output and indicates that if such schemes are to be used they should be designed and tested with this application in mind. [ABSTRACT FROM AUTHOR]

Using a variable-resolution option within the National Center for Atmospheric Research/Department of Energy Community Atmosphere Model (CAM) Spectral Element (SE) global model, a refined nest at 0.25° (∼28 km) horizontal resolution located over the North Atlantic is embedded within a global 1° (∼111 km) grid. The grid is designed such that fine grid cells are located where tropical cyclones (TCs) are observed to occur during the Atlantic TC season (June-November). Two simulations are compared, one with refinement and one control case with no refinement (globally uniform 1° grid). Both simulations are integrated for 23 years using Atmospheric Model Intercomparison Protocols. TCs are tracked using an objective detection algorithm. The variable-resolution simulation produces significantly more TCs than the unrefined simulation. Storms that do form in the refined nest are much more intense, with multiple storms strengthening to Saffir-Simpson category 3 intensity or higher. Both count and spatial distribution of TC genesis and tracks in the variable-resolution simulation are well matched to observations and represent significant improvements over the unrefined simulation. Some degree of interannual skill is noted, with the variable-resolution grid able to reproduce the observed connection between Atlantic TCs and the El Niño-Southern Oscillation (ENSO). It is shown that Genesis Potential Index (GPI) is well matched between the refined and unrefined simulations, implying that the introduction of variable-resolution does not affect the synoptic environment. Potential 'upscale' effects are noted in the variable-resolution simulation, suggesting stronger TCs in refined nests may play a role in meridional transport of momentum, heat, and moisture. [ABSTRACT FROM AUTHOR]

ABSTRACT India gets the maximum amount of rainfall during the months of June to September ( JJAS) which is known as the summer monsoon season. The erratic nature of Indian summer monsoon rainfall ( ISMR), in terms of both rainfall amount and distribution, is highly responsible for the interannual variability in agricultural production as well as occurrence of floods and droughts. Accurate seasonal predictions of ISMR are required for appropriate hydrological planning and disaster management systems. Studies have revealed that probabilistic prediction, based on the products of General Circulation Models ( GCMs), can be generated in a parametric as well as non-parametric manner. The present paper discusses the enhancement of probabilistic prediction by improving the potential predictable signal obtained from these GCMs. A Singular- Value- Decomposition based multiple linear regression method ( SVD-MLR) has been applied to improve the signal and a simple average of all GCMs ( EM) has been used as the benchmark to examine the skill of the SVD-MLR method. The potential of the proposed method has been assessed through Brier Skill Score ( BSS) and Rank Probability Skill Score ( RPSS). A rigorous analysis has finally revealed that SVD-MLR method has better skill than EM in predicting the typical nature of observed monsoon rainfall in extreme years. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, precipitation outputs from retrospective seasonal forecasts made by nine General Circulation Models ( GCMs) are used to investigate historical Indian summer monsoon seasonal rainfall variability and predictability over India. The observed data is taken from the India Meteorological Department whereas GCMs are obtained from the International Research Institute for Climate and Society, Columbia University, the National Center for Environmental Prediction, and the Japan Agency for Marine Earth Science and Technology. The study focusses on June- September precipitation hindcasts initialized from the 1 May. First, the mean climatology, variance of interannual variability ( IAV), and long-term trends for the nine GCMs were evaluated. Then Empirical Orthogonal Function ( EOF) is used to extract major climate modes and spectral analyses method is used to investigate the temporal properties of the leading principal components. It is found that the models are able to reproduce the climatology and IAV to varying degrees. The EOF and spectral analyses of models hindcast reveal that these models are capable of predicting the observed precipitation variability to some extent. In order to study the remote response, the correlation co-efficient between model predicted rainfall and sea surface temperature ( SST) have been calculated. The results suggest that the models show exaggerated remote response to ENSO SST forcing and the Indian Ocean Dipole Mode index has less predictive skill compared to ENSO. The correlation values between the model predicted Monsoon Hadley Index ( MHI) and observed MHI reveals that only a few of them could exhibit large scale circulation features well. [ABSTRACT FROM AUTHOR]

Two statistical downscaling models were developed for downscaling monthly General Circulation Model ( GCM) outputs to precipitation at a site in north-western Victoria, Australia. The first downscaling model was calibrated and validated with the National Centers for Environmental Prediction/National Center for Atmospheric Research ( NCEP/NCAR) reanalysis outputs over the periods of 1950-1989 and 1990-2010 respectively. The projections of precipitation into the future were produced by introducing the outputs of HadCM3, ECHAM5, GFDL2.0 and GFDL2.1, pertaining to A2 and B1 greenhouse gas emission scenarios to this downscaling model. In this model, the input data used in the development and future projections are not homogeneous, as they originate from two different sources. As a solution to this issue, the second downscaling model was developed and precipitation projections into the future were produced with a homogeneous set of inputs. To produce a homogeneous set of inputs to this model, regression relationships were formulated between the NCEP/NCAR reanalysis outputs and the twentieth-century climate experiment outputs corresponding to the variables used in the first downscaling model obtained from the ensemble consisting of HadCM3, ECHAM5 and GFDL2.0. The outputs of these relationships pertaining to the periods of 1950-1989 and 1990-1999 were used for the calibration and validation of this downscaling model respectively. Using the outputs of HadCM3, ECHAM5 and GFDL2.0 pertaining to A2 and B1 emission scenarios on these relationships, inputs for the second downscaling model pertaining to the period of 2000-2099 were generated. The first downscaling model with NCEP/NCAR reanalysis outputs showed a high Nash-Sutcliffe Efficiency ( NSE) of 0.75 over the period 1950-1999. When this downscaling model was run with the twentieth-century climate experiment outputs of HadCM3, ECHAM5, GFDL2.0 and GFDL2.1, it exhibited limited performances over the period 1950-1999, which was indicated by relatively low NSEs of −0.62, −2.54, −0.40 and −0.48 respectively. The second downscaling model displayed an NSE of 0.35 over the period 1950-1999. [ABSTRACT FROM AUTHOR]

This study employed least square support vector machine regression (LS-SVM-R) and multi-linear regression (MLR) for statistically downscaling monthly general circulation model (GCM) outputs directly to monthly catchment streamflows. The scope of the study was limited to calibration and validation of the downscaling models. The methodology was demonstrated by its application to a streamflow site in the Grampian water supply system in northwestern Victoria, Australia. Probable predictors for the study were selected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set based on the past literature and hydrology. Probable variables that displayed the best significant correlations, consistently with the streamflows over the entire period of the study (1950-2010) and under three 20-year time slices (1950-1969, 1970-1989 and 1990-2010) were selected as potential predictors. To better capture seasonal variations of streamflows, downscaling models were developed for each calendar month. The standardized potential predictors were introduced to the LS-SVM-R and MLR models, starting from the best correlated three and then, others one by one, based on their correlations with the streamflows, until the model performance in validation was maximized. This stepwise model development enabled the identification of the optimum number of potential variables for each month. The model calibration was performed over the period 1950-1989 and validation was done for 1990-2010. LS-SVM-R model parameter optimization was achieved using simplex algorithm and leave-one-out cross-validation. The MLR models were optimized by minimizing the sum of squared errors. In both modelling techniques, validation was performed as an independent simulation. In calibration, LS-SVM-R and MLR models displayed equally good performances with a trend of under-predicting high flows. During validation, LS-SVM-R outperformed MLR, though both techniques over-predicted most of the streamflows. It was concluded that LS-SVM-R is a better technique for statistically downscaling GCM outputs to streamflows than MLR, but still MLR is a potential technique for the same task. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Projecting agricultural crop, such as bread wheat, yield under future climate plays a vital role in planning for supply and demand, especially in developing countries. The objective of this study was to investigate impacts of climate change on grain yield of rainfed wheat in the Kashafrood basin situated in northeastern Iran. Climate projections of two General Circulation Models (HadCM3 and CGCM2) under two climate change scenarios (A2 and B2) were employed in this study. A statistical downscaling method was employed for developing the quantitative relationship between large-scale atmospheric variables (predictors) and local variables (observations), also daily climatological parameters performed by the LARS-WG5 stochastic weather generator, the Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.5 adjusted to evaluate rainfed wheat performance. Results of this study represented substantial difference between GCM models and scenarios for wheat yield in the study area. The A2 scenario indicated more negative impact on wheat yield than the B2 using both GCM models. Highest increase and decrease of grain yield in comparison with the baseline belongs to the 2010-2039 period (+15%) using the HadCM3 model under the B2 scenario and the HadCM3 model under A2 in the 2040-2069 period (−50%), respectively. In general, growth period length indicated a markedly decreasing trend in both periods. There were strong relations between wheat yield and precipitation rate and growth period length in this study. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Global climate models are constantly being upgraded, but it is often not clear what these changes have on climate change impact projections. We used difference maps to directly compare downscaled projections of temperature and precipitation across North America for two versions (or generations) of three different Atmospheric-Ocean General Circulation Models (AOGCM)s. We found that AOGCM versions differed in their projections for the end of the current century by up to 4 °C for annual mean temperature and 60% for annual precipitation. To place these changes in an ecological context, we reanalyzed our work on shifts in tree climate envelopes (CEs) using the newer-generation AOGCM projections. Based on the updated AOGCMs, by the 2071-2100 period, tree CEs shifted up to 2.4 degrees further north or 2.6 degrees further south (depending on the AOGCM) and were about 10% larger in size. Despite considerable differences between versions of a given AOGCM, projections made by the newer version of each AOGCM were in general agreement, suggesting convergence across the three models studied here. Assessing the AOGCM outputs in this way provides insight into the magnitude and importance of change associated with AOGCM upgrades as they continue to evolve through time. [ABSTRACT FROM AUTHOR]