Hadley Centre Research Articles

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Hydrological response to climate change in Baro basin, Ethiopia, using representative concentration pathway scenarios

Droughts and floods are common in the Baro basin and climate change may exacerbate them. This study aimed to investigate the hydrological response to climate change’s impact in the Baro River basin. Four climate models namely, Hadley Centre Global Environmental Model, version 2 (HadGEM2-ES), Max Planck Institute Earth System Model—Low Resolution (MPI-ESM-LR), Coupled Model Version 5, Medium Resolution (CM5A-MR) and European Community Earth System Model (EC-Earth) dynamically downscaled outputs were obtained from Africa coordinated regional downscaling experiment program. The four climate models were evaluated using a suite of statistical measures such as bias, Root Mean Squared Error, and Coefficient of Variation. The bias of the simulated rainfall varies between − 4.20% and − 25.39% suggesting underestimation. The performance of the models differs subject to the performance measures used for evaluation. Before being used in the climate impact analysis, the climate model data was heavily skewed and needed correction. In terms of bias, HadGEM2-ES performed the worst while EC-Earth performed the best. MPI-ESM-LR was the worst performer in terms of RMSE and CM5A-MR was the best. Changes in the hydrological response to climate change were compared to the baseline scenario (1971–2000) under the Representative Concentration Pathway Scenarios (RCP 4.5) for the medium term (2041–2070). The GCM predictions for the RCP 4.5 scenarios suggested that, in the medium period (2041–2070) the maximum temperature in the Baro River basin will probably rise by 2.1 °C for MPI-ESM-LR and 2.49 °C for CM5A-MR, while the minimum temperature would likely climb by 1.7 °C to EC-Earth and 2.8 °C for HadGEM2-ES. Annual rainfall is expected to fall by 7.02% for CM5A-MR and 17.01% for HadGEM2-ES, while annual evapotranspiration potential is likely to rise. Except from March to May CM5A-MR consistently generated the greatest amount of streamflow change, while MPI-ESM-LR consistently generated the highest magnitude of streamflow change. The annual streamflow reduction is consistent with the annual precipitation reduction and increased annual potential evapotranspiration. Generally, climate change is predicted to have a significant impact on the hydrological response in the Baro River basin under the RCP 4.5 scenario.

GERB Obs4MIPs: a dataset for evaluating diurnal and monthly variations in top-of-atmosphere radiative fluxes in climate models

Abstract. A newly available radiative flux dataset specifically designed to enable the evaluation of the diurnal cycle in top-of-atmosphere (TOA) fluxes as captured by climate and Earth system models is presented. Observations over the period 2007–2012 made by the Geostationary Earth Radiation Budget (GERB) instrument are used to derive monthly hourly mean outgoing longwave radiation (OLR) and reflected shortwave (RSW) fluxes on a regular 1° latitude–longitude grid covering approximately 60° N–60° S and 60° E–60° W. The impact of missing data is evaluated in detail, and a data-filling solution is implemented using estimates of broadband fluxes from the Spinning Enhanced Visible and Infrared Imager flying on the same Meteosat platform, scaled to the GERB observations. This relatively simple approach is shown to deliver an approximate improvement by a factor of 10 in both the bias caused by missing data and the associated variability in the error. To demonstrate the utility of this V1.1 filled GERB Observations for Climate Model Intercomparison Projects (Obs4MIPs) dataset, comparisons are made to radiative fluxes from two climate configurations of the Hadley Centre's Global Environmental Model: HadGEM3-GC3.1 and HadGEM3-GC5.0. Focusing on marine stratocumulus and deep convective cloud regimes, diurnally resolved comparisons between the models and observations highlight discrepancies between the model configurations in terms of their ability to capture the diurnal amplitude and the phase in TOA fluxes, details that cannot be diagnosed by comparisons at lower temporal resolutions. For these cloud regimes the GC5.0 configuration shows improved fidelity to the observations relative to GC3.1, although notable differences remain. The V1.1 filled GERB Obs4MIPs monthly hourly TOA fluxes are available from the Centre for Environmental Data Analysis, with the OLR fluxes accessible at https://doi.org/10.5285/90148d9b1f1c40f1ac40152957e25467 (Bantges et al., 2023a) and the RSW fluxes accessible at https://doi.org/10.5285/57821b58804945deaf4cdde278563ec2 (Bantges et al., 2023b).

The HadGEM3‐GC3.1 Contribution to the CMIP6 Detection and Attribution Model Intercomparison Project

AbstractThe UK contribution to the Detection and Attribution Model Intercomparison Project (DAMIP), part of the sixth phase of the Climate Model Intercomparison Project (CMIP6), is described. The lower atmosphere and ocean resolution configuration of the latest Hadley Centre global environmental model, HadGEM3‐GC3.1, is used to create simulations driven either with historical changes in anthropogenic well‐mixed greenhouse gases, anthropogenic aerosols, or natural climate factors. Global mean near‐surface air temperatures from the HadGEM3‐GC31‐LL simulations are consistent with CMIP6 model ensembles for the equivalent experiments. While the HadGEM3‐GC31‐LL simulations with anthropogenic and natural forcing factors capture the overall observed warming, the lack of marked simulated warming until the 1990s is diagnosed as due to aerosol cooling mostly offsetting the well‐mixed greenhouse gas warming until then. The model has unusual temperature variability over the Southern Ocean related to occasional deep convection bringing heat to the surface. This is most prominent in the model's aerosol only simulations, which have the curious feature of warming in the high southern latitudes, while the rest of the globe cools, a behavior not seen in other CMIP6 models. This has implications for studies that assume model responses, from different climate drivers, can be linearly combined. While DAMIP was predominantly designed for detection and attribution studies, the experiments are also very valuable for understanding how different climate drivers influence a model, and thus for interpretating the responses of combined anthropogenic and natural driven simulations. We recommend institutions provide model simulations for the high priority DAMIP experiments.

Ensemble of global climate simulations for temperature in historical, 1.5 °C and 2.0 °C scenarios from HadAM4

Large ensembles of global temperature are provided for three climate scenarios: historical (2006–16), 1.5 °C and 2.0 °C above pre-industrial levels. Each scenario has 700 members (70 simulations per year for ten years) of 6-hourly mean temperatures at a resolution of 0.833° ´ 0.556° (longitude ´ latitude) over the land surface. The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5. The data is stored within the UK Natural and Environmental Research Council Centre for Environmental Data Analysis repository as NetCDF V4 files.

Interdecadal variations in the interannual relationship between winter tropical Pacific SST and subsequent summer Arctic sea ice in early 2000s

AbstractThe present study investigates the interdecadal change in the interannual relationship between winter sea surface temperature (SST) in the tropical central‐eastern Pacific and the subsequent summer Arctic sea ice concentration (SIC) and its possible mechanism using 1979–2023 monthly SST and SIC data from Hadley Center and atmospheric reanalysis data from NCEP/NCAR. It is found that the relationship between the winter tropical Pacific SST and the subsequent summer SIC of Beaufort Sea experienced an obvious interdecadal change from a significant negative correlation to a weak and insignificant correlation before and after the early 2000s, respectively. Before the early 2000s, when winter SST warming (cooling) anomalies occurred in the central and eastern tropical Pacific, it could sustain into summer and enhance a Rossby wave propagating from Tropic to high latitude, forming negative (positive) pressure anomalies in the North Pacific. Influenced by such anomalies, it is conducive to maintaining negative (positive) SST anomalies in the North Pacific from winter to subsequent summer. The summer SST cooling (warming) in the North Pacific could form a favourable atmospheric circulation anomaly for less (more) SIC. In contrast, after the early 21st century, the strong SST anomalies in winter over the eastern tropical Pacific would weaken with time and subsequently diminish in spring, leading to the SST anomalies in the North Pacific persist to spring, which can hardly affect summer SIC anomaly in the Beaufort Sea. Therefore, the relationship collapses after the the early 2000s due to the weakened persistence of the eastern tropical Pacific SST anomaly.

Extreme rainfall and landslides as a response to human-induced climate change: a case study at Baixada Santista, Brazil, 2020

In March 2020, an extreme rainfall in Baixada Santista, Brazil, led to a series of landslides affecting more than 2800 people and resulting losses exceeding USD 43 million. This attribution study compared extreme rainfall in two large ensembles of the UK Met Office Hadley Centre HadGEM3-GA6 model that represented the event with and without the effects of anthropogenic climate change. Antecedent rainfall conditions on two different timescales are considered, namely extreme 60-day rainfall (Rx60day) which relates to the soil moisture conditions and extreme 3-day rainfall (Rx3day) which represents landslide triggering heavy rainfall. In the scenario including both natural and human-induced factors the antecedent 60 day rainfall became 74% more likely, while the short-term trigger was 46% more likely. The anthropogenic contribution to changes in rainfall accounted for 20–42% of the total losses and damages. The greatest economic losses occurred in Guarujá (42%), followed by São Vicente (30%) and Santos (28%). Landslides were responsible for 47% of the homes damaged, 85% of the homes destroyed, all reported injuries, and 51% of the deaths associated with heavy rainfall. Changes in land cover and urbanization showed a pronounced increase in urbanized area in Guarujá (107%), São Vicente (61.7%) and Santos (36.9%) and a reduction in farming area. In recent years, the region has experienced an increase in population growth and a rise in the proportion of irregular and/or precarious housing in high-risk areas. Guarujá has the highest number of such dwellings, accounting for 34.8%. Our estimates suggest that extreme precipitation events are having shorter return periods due to climate change and increased urbanization and population growth is exposing more people to these events. These findings are especially important for decision-makers in the context of disaster risk reduction and mitigation and adaptation to climate change.

“Climate Change Projections in Jammu and Kashmir under A1B, A2 & B2 Scenarios”

Abstract: The Global Climate Model (GCM) run at a coarse spatial resolution cannot be directly used for climate impact studies. Downscaling is required to extract the sub-grid and local scale information. The present study was undertaken to study the effect of climate change on weather parameters like maximum temperature, minimum temperature and precipitation for A1B, A2 and B2 scenarios. The study uses the Hadley centre coupled model (HadCM3) of the Intergovernmental Panel for Climate Change (IPCC) Forth Assessment Report. Thirty year weather data (1985-2015) obtained form Indian Metrological Department (IMD) Srinagar was used for the study. Statistical Downscaling Model (SDSM), R software and Artificial Neural Network (ANN) models were tested for the Srinagar area. This research investigates which among the three is better model for downscaling climate data for Srinagar area. The modelling results showed a first rate agreement between the experimental data and predicted values for temperature series with high coefficient of determination R2 values varying from (0.93-0.95) for different models. In case of precipitation R2 values varied from (0.08-0.249) for different models. The low values of coefficient of determination in precipitation time series are due to lot of uncertainty. occurrence of precipitation which could not be defined by the selected models. Based on Mean squared error (MSE), Root mean squared error (RMSE), Absolute average deviation (AAD), correlation coefficient and coefficient of determination, the R software performed better than the SDSM and ANN for maximum temperature, minimum temperature & precipitation. Thus R software was used for climate scenario generation. According to our simulated model, precipitation showed a decreasing trend whereas maximum and minimum temperatures showed an increasing trend. An overall increasing pattern of (9.81%) for A1B scenario, (15.24%) for A2 scenario & (10.32%) for B2 scenario for maximum temperature, (2.18%) for A1B scenario, (29.15%) for A2 scenario & (19.90%) for B2 scenario for minimum temperature and an overall decreasing pattern (22.60%) for A1B scenario, (17.23%) for A2 scenario & (7.11%) for B2 scenario for precipitation was noted

Assessing climate change impacts on the spatial distribution of Castanea sativa Mill. using ecological niche modeling

In recent decades, ecological niche modeling (ENM) has become integral for assessing climate change impacts on species distributions. In this study we conducted a comprehensive ENM using the Kuenm R package, employing MaxEnt as the modeling algorithm, to evaluate the impact of climate change on the habitat of Castanea sativa Mill., a non-wood forest species of high commercial interest in Turkiye, within the limits of the Trabzon Regional Directorate of Forestry (RDF). Predictors related to the species' ecology were carefully selected. The Future distributions of C. sativa for 2061–2080 under Shared Socio-economic Pathways (SSPs) 1-2.6, 2-4.5, and 5-8.5 were modeled using predictions from the Hadley Centre Global Earth Model HadGEM-GC31-LL. Extensive calibration modeling with Kuenm resulted in 434 models, and the most robust model, determined by statistical significance, predictive power, and complexity, revealed a drastic reduction in suitable areas for C. sativa (ranging from 86% to 99% across SSPs). The critical values of bio1 and bio5 were identified as primary factors. Predictions suggest potential migration of C. sativa to higher latitudes or elevations seeking more favorable climatic conditions. The substantial reduction in habitat suitability, even under SSP1-2.6, poses a significant threat, emphasizing the need for urgent measures to mitigate climate change impacts and ensure the species' survival and continuity.

Inter-Comparison of Satellite-Based Sea Ice Concentration in the Amundsen Sea, Antarctica

We conducted a comparison of sea ice concentration (SIC) in the Amundsen Sea using three satellite datasets: Hadley Centre’s sea ice and sea surface temperature (HadISST1), Operational Sea Surface Temperature and Ice Analysis (OSTIA), and Advanced Microwave Scanning Radiometer 2 (AMSR2). HadISST1 has the longest time period, while AMSR2 has the shortest. In terms of grid resolution, HadISST1 has the coarsest resolution, while AMSR2 has the finest. The sea ice areas (SIAs) observed in HadISST1, OSTIA, and AMSR2 are similar. We studied the decadal variations in SICs by dividing the study period into four temporal segments. We investigated the differences between HadISST1 and OSTIA for each temporal segment. HadISST1 exhibited a more pronounced positive trend compared to OSTIA between 2005 and 2010. Additionally, we compared the interannual and seasonal variations in SICs between HadISST1 and OSTIA. Lastly, it should be noted that the Amundsen Sea polynya area varies across all three datasets.

Recent Strengthening of the ENSO Influence on the Early Winter East Atlantic Pattern

Previous studies have demonstrated that the influence of the El Niño–Southern Oscillation (ENSO) on the Euro-Atlantic atmospheric circulation varies considerably during the boreal winter. Compared to the late winter (January–March) relationship, the early winter (November–December) teleconnection is more uncertain and less understood. In this paper, we revisited this early winter regional ENSO teleconnection using the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) and the European Centre for Medium-Range Weather Forecasting (ECMWF) fifth generation reanalysis (ERA5) datasets for the period 1979–2022. It was found that the signal projected well onto the second dominant mode of Euro-Atlantic atmospheric variability, the East Atlantic Pattern (EAP), rather than the previously mentioned North Atlantic Oscillation (NAO). This influence is associated with ENSO-induced dipolar convection anomalies in the Gulf of Mexico and Caribbean Sea (GMCA), which leads to an EAP via exciting Rossby waves propagating northward into the North Atlantic. We further revealed that this ENSO–EAP teleconnection underwent a pronounced interdecadal strengthening around the late 1990s. Prior to the late 1990s, the convective response to ENSO in the GMCA was weak. The atmospheric responses over the Euro-Atlantic were mainly driven by the ENSO-induced convective forcing in the tropical Indian Ocean, which favors an NAO-like pattern. In contrast, since the late 1990s, ENSO has induced stronger precipitation anomalies in the GMCA, which exert a dominant influence on the Euro-Atlantic atmospheric circulation and produce an EAP. These results have useful implications for the further understanding of ENSO-related early winter atmospheric and climate variability in the Euro-Atlantic region.

Climate Adaptation Planning: Developing a Methodology for Evaluating Future Climate Change Impacts on Museum Environments and Their Collections

As organisations, museums are responsible for conserving, protecting, and displaying artwork and artefacts. Museum buildings must deliver an environment that will continue to provide this facility for both current and future generations. This research focused on presenting a museum with quantifiable and measurable data to help with climate adaptation planning. A methodology was developed using monitored data. Subhourly data for both indoor and outdoor temperature and humidity spanning the years 2012–2021 was used to produce a daily maximum, daily minimum, and daily average dataset. A sensitivity analysis determined which years to use to derive the indoor-outdoor relationships used in climate modelling. Future impacts were calculated using UK Climate Projections 2018 (UKCP18) data (12 models on a 2.2 km scale), as published by the Met Office Hadley Centre. The data contained within the 12 models was overlayed with the relationships derived to calculate the projected indoor temperature and humidity conditions within the museum. The results presented indicate that temperature and humidity conditions are projected to exceed design conditions more frequently in the coming decades. Consequently, adaptation plans must consider the potential impacts that include indoor environmental deterioration, leading to discomfort and health implications, increased energy costs, and system upgrade costs, as well as the potential for accelerated degradation of artwork and artefacts.

Predicting facial eczema risks in a changing New Zealand climate

Facial eczema is a major concern for New Zealand farmers due to its economic impact and animal welfare implications. The disease occurs when animals ingest sporidesmin, a mycotoxin produced by spores of the fungus Pseudopithomyces chartarum. Spore production is related to weather conditions; thus the incidence and severity of facial eczema varies between years, with thedisease commonly occurring from late summer through autumn in the North Island. We developed a simple model to estimate climatic suitability for P. chartarum sporulation and ran it using climate data for 2008-2021 to compare its estimates with spore counts from the same years. Model climatic suitability estimates had significant linear correlations with an index of exposure to spores derived from spore counts at both national and local scales. Model results were also consistent with a documented outbreak of facial eczema. Using predicted future climate data from the Hadley Centre Global Environment Model version 2 and two emissionsscenarios, the model suggested climatic suitability for P. chartarum sporulation will increase with time in many New Zealand regions, particularly in the southern North Island and eastern parts of the South Island. However, it could remain relatively static in some other areas, thus the degree of change in climatic suitability for P. chartarum sporulation is predicted to vary between New Zealand regions.

Evaluation of statistical downscaling model's performance in projecting future climate change scenarios

Abstract Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many statistical downscaling models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD model. Thus, it is desirable to compare the performance of SD models to ensure their adaptability in future climate studies. In this study, a statistical downscaling model (SDSM) or multi-linear regression and the Least Square Support Vector Machine (LS-SVM) were used to do downscaling and compare the results with those obtained from general circulation model (GCM) for identifying the best SD model for the Indira Sagar Canal Command area located in Madhya Pradesh, India. The GCM, Hadley Centre Coupled Model version 3 (HadCM3), was utilized to extract and downscale precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) for 1961–2001 and then for 2001–2099. Before future projections, both SD models were initially calibrated (1961–1990) and validated (1991–2001) to evaluate their performance for precipitation and temperature variables at all gauge stations, namely Barwani, East Nimar, and West Nimar. Results showed that the precipitation trend was under-predicted owing to large errors in downscaling, while temperature was over-predicted by SD models.

Sea surface temperature trends for Tampa Bay, West Florida Shelf and the deep Gulf of Mexico

Sea surface temperatures for Tampa Bay, the West Florida Continental Shelf (WFS) and the adjacent deep Gulf of Mexico are examined for trends. Data sets are from stations maintained by the Hillsborough County Environmental Protection Commission, buoys maintained by the University of South Florida Coastal Ocean Monitoring and Prediction System and the National Oceanic and Atmosphere Administration (NOAA) National Data Buoy Center, the Optimum Interpolation Sea Surface Temperature analyses by the NOAA National Centers for Environmental Information, and the Hadley Centre Sea Surface Temperature. These various data sets, each with different record lengths, require the consideration of trends both on the basis of record length and start time. Tampa Bay shows a warming trend, but with considerable inter-annual variability and start time bias resulting in a lack of statistical significance in more recent years. The WFS is also generally warming, and its inter-annual variability is largely controlled by the upwelling of cooler, deeper Gulf of Mexico water across the shelf break. The deep GOM shows statistically significant warming in most of the data except for the “gappy” records from buoys, both along the continental shelf and in the deep water. Trends in the Gulf of Mexico are mostly between 0.1 and 0.5 °C/decade, somewhat larger than the secular rise found globally, although within the range of the observed decadal variability.

Future Climate Prediction Based on Support Vector Machine Optimization in Tianjin, China

Climate is closely related to human life, food security and ecosystems. Forecasting future climate provides important information for agricultural production, water resources management and so on. In this paper, historical climate data from 1962–2001 was used at three sites in Tianjin Baodi, Tianjin and Tanggu districts as baseline and the model parameters were calibrated by the Long Ashton Research Station Weather Generator (LARS-WG). 2m-temperatures in 2011–2020 were verified under two scenarios, representative concentration pathway (RCP) 4.5 and RCP8.5 in different atmospheric circulation models with optimal minimum 2m-temperatures at the three sites. From 2031–2050, Tianjin will be using more moderate minimum 2m-temperatures in future simulations. Support vector machines (SVM) were used to optimize the simulated data to obtain more accurate future maximum and minimum 2m-temperatures for the three sites. The results showed that the determinant coefficient of LARS-WG simulation was 0.8 and SVM optimized determinant coefficient was 0.9 which greatly improved the prediction accuracy. The minimum and maximum future 2m-temperatures optimized under European Community Earth System Model (EC-EARTH) were relatively low and the same future 2m-temperatures optimized under Hadley Centre Global Environment Model Earth System (Had-GEM2-ES4) were high especially in the RCP8.5 scenario which simulated 2051–2070 climate. The SVM optimization showed that the maximum and minimum 2m-temperatures were in general agreement with the original simulation values.

Understanding the Mechanisms for Tropical Surface Impacts of the Quasi‐Biennial Oscillation (QBO)

AbstractThe impact of the quasi‐biennial oscillation (QBO) on tropical convection and precipitation is investigated through nudging experiments using the UK Met Office Hadley Center Unified Model. The model control simulations show robust links between the internally generated QBO and tropical precipitation and circulation. The model zonal wind in the tropical stratosphere was nudged above 90 hPa in atmosphere‐only and coupled ocean‐atmosphere configurations. The convection and precipitation in the atmosphere‐only simulations do not differ between the experiments with and without nudging, which may indicate that SST‐convection coupling is needed for any QBO influence on the tropical lower troposphere and surface. In the coupled experiments, the precipitation and sea‐surface temperature relationships with the QBO phase disappear when nudging is applied. Imposing a realistic QBO‐driven static stability anomaly in the upper‐troposphere lower‐stratosphere is not sufficient to simulate tropical surface impacts. The nudging reduced the influence of the lower troposphere on the upper branch of the Walker circulation, irrespective of the QBO, indicating that the upper tropospheric zonal circulation has been decoupled from the surface by the nudging. These results suggest that grid‐point nudging mutes relevant feedback processes occurring at the tropopause level, including high cloud radiative effects and wave mean flow interactions, which may play a key role in stratospheric‐tropospheric coupling.

Combining local model calibration with the emergent constraint approach to reduce uncertainty in the tropical land carbon cycle feedback

Abstract. The role of the land carbon cycle in climate change remains highly uncertain. A key source of the projection spread is related to the assumed response of photosynthesis to warming, especially in the tropics. The optimum temperature for photosynthesis determines whether warming positively or negatively impacts photosynthesis, thereby amplifying or suppressing CO2 fertilisation of photosynthesis under CO2-induced global warming. Land carbon cycle models have been extensively calibrated against local eddy flux measurements, but this has not previously been clearly translated into a reduced uncertainty in terms of how the tropical land carbon sink will respond to warming. Using a previous parameter perturbation ensemble carried out with version 3 of the Hadley Centre coupled climate–carbon cycle model (HadCM3C), we identify an emergent relationship between the optimal temperature for photosynthesis, which is especially relevant in tropical forests, and the projected amount of atmospheric CO2 at the end of the century. We combine this with a constraint on the optimum temperature for photosynthesis, derived from eddy covariance measurements using the adjoint of the Joint UK Land Environment Simulator (JULES) land surface model. Taken together, the emergent relationship from the coupled model and the constraint on the optimum temperature for photosynthesis define an emergent constraint on future atmospheric CO2 in the HadCM3C coupled climate–carbon cycle under a common emissions scenario (A1B). The emergent constraint sharpens the probability density of simulated CO2 change (2100–1900) and moves its peak to a lower value of 497 ± 91 compared to 607 ± 128 ppmv (parts per million by volume) when using the equal-weight prior. Although this result is likely to be model and scenario dependent, it demonstrates the potential of combining the large-scale emergent constraint approach with a parameter estimation using detailed local measurements.

MaxEnt modeling for predicting the potential distribution of Lebrunia bushaie Staner (Clusiaceae) under different climate change scenarios in Democratic Republic of Congo

Lebrunia bushaie Staner is an endangered medicinal plant that is endemic in the Democratic Republic of Congo (DRC). In this study, its current and future suitable distribution habitats were assessed using two Global Circulation Models (GCMs) HadGEM3-GC3.1-LL” (Third Hadley Centre Global Environment Model in the Global Coupled Configuration 3.1) and MIROC6 (Model for Interdisciplinary Research on Climate) under twoShared Socioeconomic Pathways (ssp) scenarios, ssp126 and ssp245 at the horizon 2050. Potential habitat predictions for L. bushaie were performed using MaxEnt software. The area under the curve (AUC˃ 0.90) revealed a high level of prediction for the current and future scenarios. Seasonal Temperature (Bio4), and isothermality (Bio3) were the variables with the greatest influence on the distribution of L. bushaie. The current distribution that is highly suitable for this plant corresponds to the habitat in which this species was inventoried. Furthermore, the projected future suitable area of L. bushaie based HadGEM3-GC3.1-LL and MIROC6 is expected to increase under the ssp126 and ssp245 scenarios by 2050.

The benefit of the Covid‐19 pandemic on global temperature projections

AbstractThis paper illustrates the benefit of the Covid‐19 pandemic on global temperature projections. Global temperature projections are obtained by forecasting Earth's temperature anomalies time series with spectral analysis. Time series of Earth's Temperature Anomalies of the Globe are collected by NASA/GISS and in the Northern and Southern Hemispheres by the Climatic Research Unit of the University of East Anglia and Hadley Centre. Spectral analysis aims at unveiling temperature anomalies time series by transforming them into simplified time series after decomposition, extrapolating information nested in these simplified series and rebuilding the forecasted time series. Earth's temperature anomalies are forecasted until 2100. Two sets of forecasts are generated, one with historical data including the Covid‐19 pandemic (from 1880 up to 2021 with NASA/GISS data or from 1850 up to 2022 with East Anglia and Hadley Centre data) and one excluding the Covid‐19 pandemic up to 2019. With the sets of data not including the pandemic, forecasts show temperature anomalies skyrocketing and bringing Earth's global warming at alarming levels in Northern and Southern Hemispheres. With the set of data including the pandemic, forecasts are reverting showing lower alarming levels by the end of the century, in the Northern and Southern Hemispheres and globally.