Projected Air Temperature Research Articles

Air Temperature Projections over the Mid and Southern of Iraq during the 21th Century Under Representative Concentration Pathways (RCPs), RCP 4.5 and RCP 8.5 Scenarios

Global climate conditions and hydrologic processes have changed significantly as a result of climate change, and as long as global warming persists, extreme events will probably more frequently happen. Climate change significantly affects Iraq through variations in air temperature, environment, agriculture and water supply. This study aimed to evaluate the observed period of temperature 19 80–2023 and the Projecting future trends of air temperature for mid and southern Iraq using BNU-ESM, CCSM4, HadGEM2-AO, and MIRIOC5 climate models from CMIP5 under the RCP4.5 and RCP8.5 scenarios during the periods 2016–2035, 2046–2065, and 2081–2100. The findings verified that, in each of the two RCPs scenarios temperature would increase with different levels. the results showed that the temperature anomaly increased to +15.6 °C, and +18.8 °C under RCR4.5 and RCP8.5 in the MIRIOC5 model, especially in the last decade of the twentieth century. The areas of Iraq most impacted by high temperatures are the south and southeast (Basra, Missan, Thi-Qar, and Muthanna) provinces. In the first period, On the other hand, the less effect areas for the temperature increase were (Anbar and northeast of Diyala) +2.4°C, and +4.4°C for HadGEM2-AO model under RCR4.5 and RCP8.5 respectively. Through all periods, CCSM4, and MIRIOC5 models were the most identical in results. The findings can aid in the evaluation of future drought risks in Iraq by policymakers. Developing suitable adaptation techniques to counter act climate change can also be beneficial.

Climate variability and change in Ecuador: dynamic downscaling of regional projections with RegCM4 and HadGEM2-ES for informed adaptation strategies

Ecuador, a country with distinct coastal (CO), highland (HL), and Amazon (AM) regions that are characterized by unique climatic, ecological, and socio-economic features is highly vulnerable to climate change. This study focuses on these three regions, highlighting their individual importance in the broader context of Ecuador's climate vulnerability. Utilizing dynamically downscaled data from the Regional Climate Model (RCM), we generated precipitation and air temperature projections for the period 2070–2099 under three different climate change scenarios. We indicate projected temperature increases across all three regions: mean temperature increases for the CO, HL and AM regions are of 1.35, 1.55, and 1.21°C, respectively. Each year, the largest temperature increases are predicted for the third quarter (June–August), with the smallest increases predicted for the last quarter (December–February). Precipitation patterns show varied changes, with CO exhibiting a positive mean daily change, in contrast to a mean negative change in the AM region. These region-specific projections underscore the differential impacts of climate change within Ecuador and highlight the necessity for tailored adaptation measures. The study's novel approach, focusing on distinct regional impacts within a single nation, offers valuable insights for policymakers, aiding in the development of effective, region-specific climate change mitigation and adaptation strategies. This targeted approach is crucial to address unique challenges faced by different regions, thereby supporting national resilience strategies.

CMIP6 Simulation-Based Daily Surface Air Temperature and Precipitation Projections over the Qinghai-Tibetan Plateau in the 21st Century

The Qinghai-Tibetan Plateau (QTP), the source of many major Asian rivers, is sensitive to climate change, affecting billions of people’s livelihoods across Asia. Here, we developed high-resolution projections of precipitation and daily maximum/minimum temperatures at 0.1° spatial resolution over the QTP. The projections are based on the output from seven global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for historical (1979–2013) and projected (2015–2100) climates across four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). An updated nonstationary cumulative distribution function matching method (called CNCDFm) was used to remove model systemic bias. We verify the necessity of taking into account altitude in downscaling processes and the validity of nonstationary bias correction. Compared to the historical period, the climate in the QTP in the 21st century is warmer (1.2–5.1 °C, for maximum surface temperature) and wetter (3.9–26.8%) according to the corrected GCM projection. For precipitation, the Indus River (IDR), Tarim River (TMR), Inner of Qiangtang Basin (IQTB), Yarlung Zangbo (YLZBR), and Qaidam Basin (QDB) showed growth well above the global average across high radiative forcing scenarios, which could have a profound impact on the regional hydrological cycle. However, there is great uncertainty in precipitation prediction, which is demonstrated by a very low signal-to-noise ratio (SNR) and a large difference between Bayesian model averaging (BMA) and multi-model averages (MMAs). This bias-corrected dataset is available for climate change impact research in the QTP at the subregion scale.

Climate projections of human thermal comfort for indoor workplaces

Climate models predict meteorological variables for outdoor spaces. Nevertheless, most people work indoors and are affected by heat indoors. We present an approach to transfer climate projections from outdoors to climate projections of indoor air temperature (Ti) and thermal comfort based on a combination of indoor sensors, artificial neural networks (ANNs), and 22 regional climate projections. Human thermal comfort and Ti measured by indoor sensors at 90 different workplaces in the Upper Rhine Valley were used as training data for ANN models predicting indoor conditions as a function of outdoor weather. Workplace-specific climate projections were modeled for the time period 2070–2099 and compared to the historical period 1970–1999 using the same ANNs, but ERA5-Land reanalysis data as input. It is shown that heat stress indoors will increase in intensity, frequency, and duration at almost all investigated workplaces. The rate of increase depends on building and room properties, the workplace purpose, and the representative concentration pathway (RCP2.6, RCP4.5, or RCP8.5). The projected increase of the mean air temperature in the summer (JJA) outdoors, by + 1.6 to + 5.1 K for the different RCPs, is higher than the increase in Ti at all 90 workplaces, which experience on average an increase of + 0.8 to + 2.5 K. The overall frequency of heat stress is higher at most workplaces than outdoors for the historical and the future period. The projected hours of indoor heat stress will increase on average by + 379 h, + 654 h, and + 1209 h under RCP2.6, RCP4.5, and RCP8.5, respectively.

Projections of Greenland climate change from CMIP5 and CMIP6

As the Arctic continues to warm faster than the global average, the Greenland ice sheet faces a warmer future without restrictions on greenhouse gas emissions. In this study, 24 CMIP5 and 21 CMIP6 models were used to analyze projections of precipitation, 2 m or near-surface air temperature (T2m), and 500 hPa geopotential height (Z500) over Greenland under medium and high emission scenarios. The multi-model ensemble means indicate that CMIP6 is a warmer output with more precipitation in Greenland under medium and high emission scenarios relative to CMIP5. At the end of the 21st century (2071–2100), annual integrated precipitation in Greenland under RCP8.5 (SSP58.5) is projected to increase by 277.9 (281.3) Gt relative to the 1986–2005 climatological mean, with a significant positive trend of 38.5 (42.9) Gt/dec (p < 0.05) post-2020, approximately twice that under RCP4.5 (SSP24.5). Under high emission scenarios, T2m will steadily increase by 5.2 (5.7) °C at the end of the 21st century and the northern amplifying effect of warming is more intense. In this context, northeast Greenland will increase the highest precipitation, with a trend of >30 mm/dec during 2020–2100. The Z500 rises higher from southeast to northwest Greenland due to global warming under medium and high emission scenarios. Model and scenario uncertainties are major sources of uncertainty at the end of the 21st century. Compared to CMIP5, the model uncertainty of CMIP6 decreases more but remained higher at the end of this century. The projections of northern Greenland are more reliable than those of other regions, with a lower total fractional uncertainty and a greater signal-to-noise ratio. Furthermore, we did not find an additional T2m increase due to internal variability. Under medium and high emission scenarios, Greenland near surface air warming is major owed to global warming. Considering the importance of current circulation anomalies and after removing the artificial increase in Z500, the decrease in ΔZ500 indicates that future warming has been underestimated under medium and high emission scenarios.

Observationally Constrained Projections of Twenty-First-Century Regional Warming in the Extratropical Northern Hemisphere

Abstract Physically based observational constraint methods can effectively reduce uncertainty in global warming projections but have not been widely applied at regional scales. We first develop and apply multivariate linear regression models for constraining projections of surface air temperature averaged over subcontinental regions in the extratropical Northern Hemisphere, based on a set of potential constraints including climatological metrics derived from tropical and subtropical low-level cloud and global average past warming trend, as well as a set of regional climate metrics previously used in the literature. We evaluate the performance of the multivariate linear regression models based on cross-validated tests using output from phases 5 and 6 of the Coupled Model Intercomparison Projects (CMIP). We find that linear regression models using global-scale low-cloud metrics alone perform more robustly than linear regression models using the past global mean warming trend or regional climate metrics as constraints. These results, while favoring global constraints over the set of regional constraints considered, do not preclude the existence of even better regional constraints for particular regions. Through model-based cross-validation, the projections constrained using low-level cloud metrics exhibit more accurate best estimate projections, narrower uncertainty ranges, and more reliable uncertainty estimates in most Northern Hemisphere regions when compared with unconstrained projections. Application of the approach to climate projections based on both Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 using observed low-cloud metrics results in considerably narrower 5%–95% uncertainty ranges of twenty-first-century warming over subcontinental Northern Hemisphere land regions compared to unconstrained projections.

The effect of projected air temperatures on concrete box girders thermal gradients and effective temperatures in Canada

This study examines the impact of environmental thermal loads on the temperature behavior of bridges, focusing specifically on concrete box girders. Currently, the Canadian Highway Bridge Design Code (CHBDC) employs a single thermal gradient model for the entire country, which fails to account for the differences in the geographical conditions in the Canadian climate regions. Accordingly, in some cases, the temperature distributions along the girder depth and their thermally induced responses might not be determined accurately. To address this issue, a transient FE thermal analysis was conducted to develop positive and negative thermal gradients and the maximum effective temperatures (METs) for concrete box girders under extreme thermal load conditions of the projected air temperature for the years 2050–2100 and the historical solar radiation. Different parametric studies were conducted to determine how the geometric parameters of box girders affect the temperature distribution and found that the girder depth is a critical factor. Based on the analysis, a formula is proposed to predict the MET for concrete box girders. In addition, different adjustments were recommended to consider the effect of girder depth on the thermal gradients and METs of the box girders.

Constraint on regional land surface air temperature projections in CMIP6 multi-model ensemble

The reliability of the near-land-surface air temperature (LSAT) projections from the state-of-the-art climate-system models that participated in the Coupled Model Intercomparison Project phase six (CMIP6) is debatable, particularly on regional scales. Here we introduce a method of constructing a constrained multi-model-ensemble (CMME), based on rejecting models that fail to reproduce observed LSAT trends. We use the CMME to constrain future LSAT projections under the Shared Socioeconomic Pathways 5–8.5 (SSP5–8.5) and 2–4.5 (SSP2–4.5), representing the high and intermediate scenarios. In comparison with the “raw” (unconstrained) CMIP6 multi-model ensemble (MME) mean, the impact of the observation-based constraint is less than 0.05oC 100 years−1 at a global scale over the second half of 21st century. However, the regional results show a wider range of positive and negative adjustments, from -1.0oC 100 years−1 to 1oC 100 years−1 under the SSP5–8.5 scenario. Although amplitude under SSP2–4.5 is relatively smaller, the CMME adjustment is similar to that under SSP5–8.5, indicating the scenario independency of the CMME impact. The ideal 1pctCO2 experiment suggests that the response of LSAT to carbon dioxide (CO2) forcing on regional scales is responsible for the MME biases in the historical period, implying the high reliability of CMME in the 21st century projections. The advantage of CMME is that it goes beyond the idea of “model democracy” assumed in MME. The unconstrained CMIP6 MME may be overestimating the risks of future warming over North America, but underestimating the risks over Asia.

Climate projections of precipitation and temperature in cities from ABC Paulista, in the Metropolitan Region of São Paulo—Brazil

Cities are increasingly vulnerable to climate change's impacts and poorly adapted to extreme variability. This study aimed to evaluate climate projections of air temperature and precipitation in seven cities in the Metropolitan Region of São Paulo that correspond to the Greater ABC Paulista. We used high-resolution models Eta-HADGEM2_ES (CMIP5), CNRM-CM6-1-HR (CMIP6), and the TerraClimate database to analyze future projections and the specific warming levels (SWLs), respectively. Model data were validated with observed data and bias was removed. A bias correction factor was generated and used in the climate projections for the different emission scenarios. The results show a consensus between the models and the SWLs (2 and 4°C) for the increase in maximum (Tmax) and minimum (Tmin) temperatures for all municipalities in ABC Paulista in different emission scenarios until the end of the 21st century. For the nearest future (2020–2040), the city of São Caetano do Sul (SCS) shows the highest positive annual anomalies of Tmax concerning the climatological period (1985–2015), for the scenario RCP4.5 (2.8°C) and the RCP8.5 (7.4°C), according to projections from the Eta-HADGEM2_ES, highlighting summer and autumn as the hottest. For precipitation, there was a consensus between the Eta-HADGEM2_ES and the CNRM-CM6-1-HR for a reduction in all scenarios and time-slices 2020–2040 and 2041–2070. The municipalities of Diadema (−78.4%) and SCS (−78%) showed the most significant reductions in December for the RCP8.5, and for SSP5-8.5, SCS shows −30.9% in December for the 2020–2040 time-slice. On the other hand, TerraClimate presents excess rain for Ribeirão Pires (+24.8%) and Santo André (+23.7%) in winter for SWL4°C. These results suggest that an increase in Tmax and Tmin, as projected, should influence the intensity of extreme heat events. Furthermore, a reduction in annual and seasonal rainfall does not mean a decrease in the region's extreme daily events that cause floods and landslides. However, it leaves an alert of water scarcity for the supply and demand of the population. ABC Paulista does not have adaptation plans to face extreme climate change. The results can contribute to the first phase of creating an adaptation plan, giving a first view of the climate threat that should intensify until the end of the twentieth century affecting the most vulnerable municipalities.

Landscape characteristics influence projected growth rates of stream‐resident juvenile salmon in the face of climate change in the Kenai River watershed, south‐central Alaska

AbstractObjectiveClimate change is affecting the distribution and productivity of Pacific salmon throughout their range. At high latitudes, warmer temperatures have been associated with increased freshwater growth of juvenile salmon, but it is not clear how long this trend will continue before further warming leads to reduced growth. To explore the potential influence of climate warming on juvenile Chinook and Coho Salmon summer growth rates in southcentral Alaska, we coupled bioenergetics models with temperature sensitivity models for streams across the Kenai River watershed.MethodsWe measured diet (n = 772 stomachs) and growth (n = 3,791 weight/length values) under current conditions and used published air temperature projections to model growth for the 2030–2039 and 2060–2069 decades.ResultWe estimated direct effects of climate warming on juvenile growth (body mass at the end of May–September study period) will be primarily negative, ranging from +5.1% to −22.8% relative to a 2010–2019 baseline. Estimated effects depended on age cohort, feeding rate, and climate scenario. However, an extended growing season from warming could mitigate or offset predicted reductions in growth during midsummer.ConclusionOur results illustrate how diverse habitats are expected to produce variation in the magnitude of climate effects throughout juvenile salmon rearing environments.

Assessment of Impacts of Climate Change on Indian Riverine Thermal Regimes Using Hybrid Deep Learning Methods

AbstractUnderstanding the riverine thermal regimes is challenging due to sparse spatiotemporal data of river water temperatures (RWTs). The development of systematic models combined with machine learning models under data‐limited context has not been intensively studied for the prediction of RWT. The present study developed hybrid models using long short‐term memory (LSTM), integrated with (a) k‐nearest neighbor (k‐NN) bootstrap resampling algorithms (kNN‐LSTM) to address the data limitations of RWT prediction and (b) discrete wavelet transform (WT) approach (WT‐LSTM) to address the time–frequency localized features of RWT prediction. The study assessed the climate change impacts on RWT using an ensemble of National Aeronautics Space Administration Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway scenarios 4.5 and 8.5 for seven major polluted river catchments of India. The hybrid kNN‐LSTM has effectively predicted RWT at monthly time scales under data limitations and outperformed the standalone LSTM, WT‐LSTM, and hybrid three‐parameter version air2stream models. The RWT increase for Tunga‐Bhadra, Musi, Ganga, and Narmada basins is predicted as 3.0, 4.0, 4.6, and 4.7°C, respectively, for 2071–2100.

The least squares method in estimating the accuracy of surface air temperature projections based on ensembles of regional climate models

The study is devoted to the search for the optimal methodical approach for bias correction of surface air temperature from real climatic indicators for the territory of Ukraine, obtained in the projections of ensembles of regional climate models (RCM) based on the use of regression analysis, namely the least squares method (LSM) with various options of its application. The procedure included: searching for weight coefficients of linear regression equations to minimize the deviation of the forecast from the observations for each model and each grid node of the 10 RCM for two climatic periods 1961—1990 and 1991—2010; obtaining, on the basis of equations with established coefficients, the averaged errors of ensembles of models for various variants of LSM application; and determining the limits of the application of such methodical approaches to the formation of an optimal ensemble.&#x0D; Among all options for using forecasting functions, it was found that the most accurate was the option of applying LSM to differences (shifts) in values between periods when one uses monthly values of the climate indicator. In general, the use of monthly values showed the best approximation of the model data to the observation data used from the E-OBS database.&#x0D; It was found that in a certain period the approximation of the LSM is significantly better than the average, but the advantage is lost if the obtained weighting factors are used in another period. For further use, the proposed approach can be modernized in the direction of more detailed clustering in time and space, which will allow adjusting the model data even closer to the observed ones. However, our results make us doubt the feasibility of applying such an approach to the forecast of climate fields, since they are not stationary and can significantly transform over time. In this case, arithmetic averaging and averaging of shifts or the delta method remain the optimal choice for forming a prognostic ensemble of RCM.

Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions

River-flow forecasts are important for the management and planning of water resources and their rational use. The present study, based on direct multistep-ahead forecasting with multiple time series specific to the XGBoost algorithm, estimates the long-term changes and forecast monthly flows of selected rivers in Ukraine. In a new, applied approach, a single multioutput model was proposed that forecasts over both short- and long-term horizons using grouped or hierarchical data series. Three forecast stages were considered: using train and test subsets, using a model with train-test data, and training with all data. The historical period included the measurements of the monthly flows, precipitation, and air temperature in the period 1961–2020. The forecast horizons of 12, 60, and 120 months into the future were selected for this dataset, i.e., December 2021, December 2025, and December 2030. The research was conducted for diverse hydrological systems: the Prut, a mountain river; the Styr, an upland river; and the Sula, a lowland river in relation to the variability and forecasts of precipitation and air temperature. The results of the analyses showed a varying degree of sensitivity among rivers to changes in precipitation and air temperature and different projections for future time horizons of 12, 60, and 120 months. For all studied rivers, variable dynamics of flow was observed in the years 1961–2020, yet with a clearly marked decrease in monthly flows during in the final, 2010–2020 decade. The last decade of low flows on the Prut and Styr rivers was preceded by their noticeable increase in the earlier decade (2000–2010). In the case of the Sula River, a continuous decrease in monthly flows has been observed since the end of the 1990s, with a global minimum in the decade 2010–2020. Two patterns were obtained in the forecasts: a decrease in flow for the rivers Prut (6%) and the Styr (12–14%), accompanied by a decrease in precipitation and an increase in air temperature until 2030, and for the Sula River, an increase in flow (16–23%), with a slight increase in precipitation and an increase in air temperature. The predicted changes in the flows of the Prut, the Styr, and the Sula rivers correspond to forecasts in other regions of Ukraine and Europe. The performance of the models over a variety of available datasets over time was assessed and hyperparameters, which minimize the forecast error over the relevant forecast horizons, were selected. The obtained RMSE parameter values indicate high variability in hydrological and meteorological data in the catchment areas and not very good fit of retrospective data regardless of the selected horizon length. The advantages of this model, which was used in the work for forecasting monthly river flows in Ukraine, include modelling multiple time series simultaneously with a single model, the simplicity of the modelling, potentially more-robust results because of pooling data across time series, and solving the “cold start” problem when few data points were available for a given time series. The model, because of its universality, can be used in forecasting hydrological and meteorological parameters in other catchments, irrespective of their geographic location.

Dynamic, downstream‐propagating thermal vulnerability in a mountain stream network: Implications for biodiversity in the face of climate change

AbstractAs climate change continues to increase air temperature in high‐altitude ecosystems, it has become critical to understand the controls and scales of aquatic habitat vulnerability to warming. Here, we used a nested array of high‐frequency sensors, and advances in time‐series models, to examine spatiotemporal variation in thermal vulnerability in a model Sierra Nevada watershed. Stream thermal sensitivity to atmospheric warming fluctuated strongly over the year and peaked in spring and summer—when hot days threaten invertebrate communities most. The reach scale (~ 50 m) best captured variation in summer thermal regimes. Elevation, discharge, and conductivity were important correlates of summer water temperature across reaches, but upstream water temperature was the paramount driver—supporting that cascading warming occurs downstream in the network. Finally, we used our estimated summer thermal sensitivity and downscaled projections of summer air temperature to forecast end‐of‐the‐century stream warming, when extreme drought years like 2020–2021 become the norm. We found that 25.5% of cold‐water habitat may be lost under high‐emissions scenario representative concentration pathway (RCP) 8.5 (or 7.9% under mitigated RCP 4.5). This estimated reduction suggests that 27.2% of stream macroinvertebrate biodiversity (11.9% under the mitigated scenario) will be stressed or threatened in what was previously cold‐water habitat. Our quantitative approach is transferrable to other watersheds with spatially replicated time series and illustrates the importance of considering variation in the vulnerability of mountain streams to warming over both space and time. This approach may inform watershed conservation efforts by helping identify, and potentially mitigate, sites and time windows of peak vulnerability.

Evaluation and projections of surface air temperature over the Tibetan Plateau from CMIP6 and CMIP5: warming trend and uncertainty

This paper compares the historical simulations and future projections of surface air temperature over the Tibetan Plateau of the updated Coupled Model Intercomparison Project phase (CMIP6) and the precedent phase of the project (CMIP5) to quantify differences in the projections under different scenarios. Model evaluation for the historical period (1961–2005) indicates that the multi-model ensemble (MME) mean of CMIP6 outperforms CMIP5 MME in simulating spatial–temporal characteristics of surface air temperature. The temperature changes relative to 1986–2005 are projected in the near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) future under Shared Socio-economic Pathway (SSP)2-4.5 and SSP5-8.5 of CMIP6 and Representative Concentration Pathway (RCP)4.5 and RCP8.5 of CMIP5. The projected temperature shows larger increases in the long-term projection compared with the near- and mid-term projection under both SSPs and RCPs. CMIP6 MME projects higher temperature changes and accelerated warming trends relative to CMIP5 MME. Additionally, the projected temperature increases and warming rates show a significant elevation dependency, especially in the long-term projection. The uncertainty for future projections is quantified by the square root of error variance (SREV) method. The results record a clear reduction in the uncertainty of CMIP6 temperature relative to CMIP5 primarily concentrated at the elevation zones of over 5000 m. The analysis of the projected temperature over the Tibetan Plateau is of great significance for policy-makers to make socio-economic adjustments for the future warming. This study is conducive to the credibility of future temperature projections for CMIP6 and enhances our comprehension of the uncertainty of SSP and RCP scenarios.

Groundwater Temperature Modelling at the Water Table with a Simple Heat Conduction Model

This study aimed at the analysis and modelling of the groundwater temperature at the water table in different regions of Slovakia. In the first part, the analysis of the long-term trends of air and soil/ground temperature to a depth of 10 m is presented. The average annual soil/groundwater temperatures at different depths were the same but lower than the annual average air temperature by about 0.8 °C. The long-term trend analysis of the air temperature and soil temperature at a depth of up to 10 m in Slovakia showed that the air and soil/ground water temperature have risen by 0.6 and 0.5 °C, respectively, per decade over the past 30 years. The second part of the study aimed at modelling the daily groundwater temperatures at depths of 0.6–15 m below the surface. The simple groundwater temperature model was constructed based on a one-dimensional differential Fourier heat conduction equation. The given model can be used to estimate future groundwater temperature trends using regional air temperature projections calculated for different greenhouse gas emission scenarios.

Projected changes in mean annual cycle of temperature and precipitation over the Czech Republic: Comparison of CMIP5 and CMIP6

The multi-model ensembles like CMIP5 or CMIP6 provide a tool to analyze structural uncertainty of climate simulations. Currently developed regional and local climate change scenarios for the Czech Republic assess the uncertainty based on state-of-the-art Global Climate Model (GCM) and Regional Climate Model (RCM) ensembles. Present study focuses on multi-model spread of projected changes in long-term monthly means and inter-annual variability of monthly mean minimum, mean and maximum daily air temperature and monthly mean precipitation. We concentrate in more detail on the simulation of CNRM-ESM2-1, the driving GCM for the convection permitting ALADIN-Climate/CZ simulation contributing to the local scenarios in very high resolution. For this GCM, we also analyze a mini-ensemble with perturbed initial conditions to evaluate the range of internal climate variability. The results for the Czech Republic reveal minor differences in model performance in the reference period whereas quite substantial inter-generation shift in projected future change towards higher air temperature and lower summer precipitation in CMIP6 comparing to CMIP5. One of the prominent features across GCM generations is the pattern of summer precipitation decrease over central Europe. Further, projected air temperature increase is higher in summer and autumn than in winter and spring, implying increase of thermal continentality of climate. On the other hand, slight increase of winter precipitation and tendency towards decrease of summer precipitation lead to projected decrease of ombric continentality. The end of 21st century projections also imply higher probability of dry summer periods, higher precipitation amounts in the cold half of the year and extremely high temperature in summer. Regarding the CNRM-ESM2-1, it is often quite far from the multi-model median. Therefore, we strictly recommend to accompany any analysis based on the simulation of nested Aladin-CLIMATE/CZ with proper uncertainty estimate. The range of uncertainty connected to internal climate variability based on one GCM is often quite large in comparison to the range of whole CMIP6 ensemble. It implies that when constructing climate change scenarios for the Central Europe region, attention should be paid not only to structural uncertainty represented by inter-model differences and scenario uncertainty, but also to the influence of internal climate variability.

Projections of future precipitation and air temperature over the Tibetan Plateau based on Coupled Model Intercomparison Project Phase 6 multimodel ensembles

AbstractIn the context of global warming and the release of projection results from the Coupled Model Intercomparison Project Phase 6 (CMIP6), it is important to conduct research on climate change on the Tibetan Plateau (TP) which exhibits unique geographic characteristics and complex climatic conditions. Using 10 general circulation models (GCMs) from CMIP6 corrected for bias by the daily translation (DT) method, we investigated the projected changes in precipitation and air temperature for the mid‐term (2031–2050) and long‐term (2061–2080) of the 21st century on the TP after evaluating the reliability of the bias correction, for four scenarios based on different combinations of shared socioeconomic pathways and representative concentration pathways. The multimodel ensembles (MMEs) indicated that the projected annual precipitation shows an increasing trend for the mid‐term and long‐term periods, with the projected increase for the long‐term primarily concentrated in areas with less precipitation. Meanwhile, the future precipitation changes exhibit a significant elevation dependency. The rate of increase of precipitation firstly fluctuates and then gradually increases along with the elevation increasing from 1,500 to 5,800 m. The valley value of rate mainly appears in two elevation zones (2,200–2,600 m and 3,400–3,800 m). The projected temporal changes in annual air temperatures reveal obvious warming over the TP. The projected warming is overall more pronounced with increase in elevation; however, it reaches a peak at approximately 5,000–5,200 m and then slows down slightly, indicating that warming is not dependent on elevation in the higher elevation zones. The present results have important implications for investigating the impacts of climate change on the water cycle in alpine regions.

The Assessment of Future Air Temperature and Rainfall Changes Based on the Statistical Downscaling Model (SDSM): The Case of the Wartburg Community in KZN Midlands, South Africa

The agriculture sector in Africa is dominated by small-scale farmers who account for about 80% of the total farms. However, small-scale farmers are vulnerable to climate change and climate variability. Their high susceptibility to climate change emanates from their inadequate ability to adapt to climate change. As a result, small-scale farmers are generally adversely impacted by climate change due to over-reliance on rainfed agriculture and natural resources. This exposure and susceptibility, however, differ across the regions due to the heterogeneity in topography, climate, access to resources, farmer resilience and adaptation capacity. Therefore, site-specific studies are encouraged to increase the awareness, resilience and adaptation capacity at the local level. The study intends to analyse historical climate (air temperature and rainfall) data from a weather station that has not been employed for climate change studies and project possible future changes in the same climate parameters due to global warming for a localised agricultural community within the sugarbelt region of KwaZulu-Natal, South Africa. The study focuses mainly on air temperature and rainfall changes to inform local farmers about potential climate changes and possible impacts of the projected climate changes on the local agricultural productivity. This study was conducted in the KwaZulu-Natal midlands of South Africa, and the Representative Climate Pathways (RCP8.5 and RCP4.5) climate projection of the CanESM2 model were used for the projection of future air temperature and rainfall trends for the 2020s, the 2040s and the 2080s. According to the results, both minimum and maximum air temperatures will continue to increase for the entire study period. The RCP8.5 results indicate that maximum and minimum air temperatures will reach a maximum range of 1.72 to 3.14 °C and 1.54 to 3.48 °C, respectively. For the rainfall, the model projects a positive trend, although all the scenarios predict a declining trend for the near future (2020s) and an increase in the 2050s. These results indicate that, in the absence of adaptation the risk of small-scale farmers, particularly for sugarcane, which is largely planted in the area, the production losses will heighten and hence increase the likelihood of increased poverty, food insecurity and unemployment.

The Use of Envi-Met for the Assessment of Nature-Based Solutions’ Potential Benefits in Industrial Parks—A Case Study of Argales Industrial Park (Valladolid, Spain)

Urbanization causes major changes in environmental systems, including those related with radiation balances and other meteorological conditions because of changes in surfaces and the physical environment. In addition, cities generate specific microclimates as a consequence of the diverse conditions within the urban fabric. Industrial parks represent vast urban areas, often neglected, contributing to the degradation of the urban environment, including poor thermal comfort as a result of soil sealing and low albedo surfaces. Nature-Based Solutions (NBS) can promote the mitigation of the anthropic effects of urbanization using nature as an inspiration. The present study, aimed at estimating the microclimate conditions in a fraction of the Argales industrial park in the city of Valladolid (Spain), with the use of the ENVI-Met software, assesses the current situation and a planned NBS scenario. Base scenario simulation results demonstrate different conditions across the simulations, with higher temperatures on sun-exposed surfaces with low albedo, and lower temperature spots, mostly associated with shadowed areas near existent buildings. After the simulation of the NBS scenario, the results show that, when compared with the base scenario, the projected air temperature changes reach reductions of up to 4.30 °C for the locations where changes are projected from impervious low albedo surfaces to shaded areas in the vicinity of trees and a water body.