Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures

Abstract. A methodology for estimating and downscaling the probability associated with the duration of heatwaves is presented and applied as a case study for Indian wheat crops. These probability estimates make use of empirical-statistical downscaling and statistical modelling of probability of occurrence and streak length statistics, and we present projections based on large multi-model ensembles of global climate models from the Coupled Model Intercomparison Project Phase 5 and three different emissions scenarios: Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. Our objective was to estimate the probabilities for heatwaves with more than 5 consecutive days with daily maximum temperature above 35 ∘C, which represent a condition that limits wheat yields. Such heatwaves are already quite frequent under current climate conditions, and downscaled estimates of the probability of occurrence in 2010 is in the range of 20 %–84 % depending on the location. For the year 2100, the high-emission scenario RCP8.5 suggests more frequent occurrences, with a probability in the range of 36 %–88 %. Our results also point to increased probabilities for a hot day to turn into a heatwave lasting more than 5 days, from roughly 8 %–20 % at present to 9 %–23 % in 2100 assuming future emissions according to the RCP8.5 scenario; however, these estimates were to a greater extent subject to systematic biases. We also demonstrate a downscaling methodology based on principal component analysis that can produce reasonable results even when the data are sparse with variable quality.

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Response of shortwave cloud radiative effect to greenhouse gases and aerosols and its impact on daily maximum temperature

10.5194/acp-2019-1159 ◽

2020 ◽

Author(s):

Tao Tang ◽

Drew Shindell ◽

Yuqiang Zhang ◽

Apostolos Voulgarakis ◽

Jean-Francois Lamarque ◽

...

Keyword(s):

Radiative Forcing ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Maximum Temperature ◽

Boreal Summer ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Sulfate Aerosols ◽

Multilinear Regression

Abstract. Shortwave cloud radiative effects (SWCRE), defined as the difference of shortwave radiative flux between all-sky and clear-sky conditions, have been reported to play an important role in influencing the Earth’s energy budget and temperature extremes. In this study, we employed a set of global climate models to examine the SWCRE responses to CO2, black carbon (BC) aerosols and sulfate aerosols in boreal summer over the Northern Hemisphere. We found that CO2 causes positive SWCRE changes over most of the NH, and BC causes similar positive responses over North America, Europe and East China but negative SWCRE over India and tropical Africa. When normalized by effective radiative forcing, the SWCRE from BC is roughly 3–5 times larger than that from CO2. SWCRE change is mainly due to cloud cover changes resulting from the changes in relative humidity (RH) and, to a lesser extent, changes in circulation and stability. The SWCRE response to sulfate aerosols, however, is negligible compared to that for CO2 and BC. Using a multilinear regression model, it is found that mean daily maximum temperature (Tmax) increases by 0.15 K and 0.13 K per W m−2 increase in local SWCRE under the CO2 and BC experiment, respectively. When domain-averaged, the SWCRE change contribution to summer mean Tmax changes was 10–30 % under CO2 forcing and 30–50 % under BC forcing, varying by region, which can have important implications for extreme climatic events and socio-economic activities.

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A Stepwise-Clustered Simulation Approach for Projecting Future Heat Wave Over Guangdong Province

Frontiers in Ecology and Evolution ◽

10.3389/fevo.2021.761251 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jiayan Ren ◽

Guohe Huang ◽

Yongping Li ◽

Xiong Zhou ◽

Jinliang Xu ◽

...

Keyword(s):

Heat Wave ◽

Climate Models ◽

Extreme Event ◽

Heat Waves ◽

Extreme Temperature ◽

Global Climate Models ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Simulation Approach

A heat wave is an important meteorological extreme event related to global warming, but little is known about the characteristics of future heat waves in Guangdong. Therefore, a stepwise-clustered simulation approach driven by multiple global climate models (i.e., GCMs) is developed for projecting future heat waves over Guangdong under two representative concentration pathways (RCPs). The temporal-spatial variations of four indicators (i.e., intensity, total intensity, frequency, and the longest duration) of projected heat waves, as well as the potential changes in daily maximum temperature (i.e., Tmax) for future (i.e., 2006–2095) and historical (i.e., 1976–2005) periods, were analyzed over Guangdong. The results indicated that Guangdong would endure a notable increasing annual trend in the projected Tmax (i.e., 0.016–0.03°C per year under RCP4.5 and 0.027–0.057°C per year under RCP8.5). Evaluations of the multiple GCMs and their ensemble suggested that the developed approach performed well, and the model ensemble was superior to any single GCM in capturing the features of heat waves. The spatial patterns and interannual trends displayed that Guangdong would undergo serious heat waves in the future. The variations of intensity, total intensity, frequency, and the longest duration of heat wave are likely to exceed 5.4°C per event, 24°C, 25 days, and 4 days in the 2080s under RCP8.5, respectively. Higher variation of those would concentrate in eastern and southwestern Guangdong. It also presented that severe heat waves with stronger intensity, higher frequency, and longer duration would have significant increasing tendencies over all Guangdong, which are expected to increase at a rate of 0.14, 0.83, and 0.21% per year under RCP8.5, respectively. Over 60% of Guangdong would suffer the moderate variation of heat waves to the end of this century under RCP8.5. The findings can provide decision makers with useful information to help mitigate the potential impacts of heat waves on pivotal regions as well as ecosystems that are sensitive to extreme temperature.

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Climate Projections Over Different Climatic Regions of Afghanistan for Shared Socioeconomic Scenarios

10.21203/rs.3.rs-1202436/v1 ◽

2022 ◽

Author(s):

Mohammad Naser Sediqi ◽

Vempi Satriya Adi Hendrawan ◽

Daisuke Komori

Keyword(s):

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Maximum Temperature ◽

Climate Projections ◽

Climatic Regions ◽

Temperature And Precipitation ◽

Intercomparison Project ◽

Precipitation And Temperature

Abstract The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2-4.5 and 5-8.5) and two future time horizons, early (2020-2059) and late (2060-2099). The Compromise Programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975-2014). Three models, namely ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2-0, showed the highest skill in simulating all three variables, and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5-2.5oC, 2.7-4.3 oC, and 4.5-5.3 oC and minimum temperature by 1.3-1.8 oC, 2.2-3.5 oC, and 4.6-5.2 oC for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively in the later period. Meanwhile, the changes in precipitation in the range of -15-18%, -36-47% and -40-68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.

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Going with the trend: forecasting seasonal climate conditions under climate change

Monthly Weather Review ◽

10.1175/mwr-d-20-0318.1 ◽

2021 ◽

Author(s):

Yawen Shao ◽

Quan J. Wang ◽

Andrew Schepen ◽

Dongryeol Ryu

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Models ◽

Daily Maximum ◽

Post Processing ◽

Seasonal Forecasts ◽

Climate Conditions ◽

Seasonal Climate

AbstractFor managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast post-processing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical post-processing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast post-processing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting post-processed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast post-processing is expected to boost user confidence in seasonal climate forecasts.

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Intercomparison of Historical Simulation and Future Projection of Rainfall and Temperature by CMIP5 and CMIP6 GCMs Over Egypt

10.21203/rs.3.rs-718538/v1 ◽

2021 ◽

Author(s):

Mohammed Magdy Hamed ◽

Mohamed Salem Nashwan ◽

Shamsuddin Shahid

Keyword(s):

Seasonal Variability ◽

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Northern Region ◽

Global Climate Models ◽

Maximum Temperature ◽

Future Projection ◽

Adaptation Measures ◽

The Future

Abstract The performances of the Global Climate Models (GCMs) of recently released Coupled Model Intercomparison Project phase 6 (CMIP6) compared to its predecessor, CMIP5 are evaluated to anticipate the expected changes in climate over Egypt, globally one of the most environmentally fragile countries due to water insecurity and climate change. Thirteen common GCMs and their multi-model ensemble (MME) of both CMIPs were used for this purpose. The future projections were compared for two radiative concentration pathways (RCP 4.5 and 8.5), and two shared socioeconomic pathways (SSP 2-4.5 and 5-8.5) scenarios. The results revealed improvement in most CMIP6 models in replicating historical rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) climatology over Egypt. The MME of the CMIPs revealed that both could reproduce the spatial distribution and seasonal variability of climate in Egypt. However, the bias in CMIP6 is much less than that for CMIP5. The uncertainty in simulating seasonal variability of rainfall and temperature was lower for CMIP6 compared to CMIP5. The future projection of rainfall using CMIP6 MME revealed a higher reduction of precipitation (4 to 10 mm) in the economically crucial northern region compared to that estimated using CMIP5 (10 to >15 mm). CMIP6 also projected a 1.5 to 2.5ºC more rise in Tmax and Tmin compared to CMIP5. The study indicates more aggravated scenarios of climate changes in Egypt than anticipated earlier, using the CMIP5 model. Therefore, Egypt needs to streamline the existing adaptation measures formulated based on CMIP5 projections.

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Geoengineering: Impact of Marine Cloud Brightening Control on the Extreme Temperature Change over East Asia

Atmosphere ◽

10.3390/atmos11121345 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1345

Author(s):

Do-Hyun Kim ◽

Ho-Jeong Shin ◽

Il-Ung Chung

Keyword(s):

East Asia ◽

Radiative Forcing ◽

Climate Models ◽

Extreme Temperature ◽

Global Climate Models ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Extreme Temperatures

We investigated the effect of artificial marine cloud brightening on extreme temperatures over East Asia. We used simulation data from five global climate models which have conducted the GeoMIP G4cdnc experiment. G4cdnc was designed to simulate an increase in the cloud droplet number concentration of the global marine lower clouds by 50% under the greenhouse gas forcing of the RCP4.5 scenario. G4cdnc decreased the net radiative forcing in the top of the atmosphere more over the ocean, alleviating the rise in mean temperature under RCP4.5 forcing. For extreme temperatures, G4cdnc reduced both the monthly minimum of daily minimum temperature (TNn) and monthly maximum of daily maximum temperature (TXx). The response of TNn was higher than that of TXx, especially in the winter, over the Sea of Okhotsk and the interior of the continent. This spatial heterogeneity and seasonality of the response were associated with sea ice–albedo and snow–albedo feedbacks. We also calculated the efficacy of warming mitigation as a measure of the relative effect of geoengineering. The efficacy for TXx was higher than that for TNn, opposite to the absolute effect. After the termination of geoengineering, both TNn and TXx tended to rapidly revert to their trend under the RCP4.5 forcing.

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Selection of global climate models for India using cluster analysis

Journal of Water and Climate Change ◽

10.2166/wcc.2016.112 ◽

2016 ◽

Vol 7 (4) ◽

pp. 764-774 ◽

Cited By ~ 6

Author(s):

K. Srinivasa Raju ◽

D. Nagesh Kumar

Keyword(s):

Cluster Analysis ◽

Minimum Temperature ◽

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Optimal Number ◽

Global Climate Models ◽

Maximum Temperature ◽

Number Of Clusters ◽

Optimal Number Of Clusters

Global climate models (GCMs) are gaining importance due to their capability to ascertain climate variables that will be useful to develop long, medium and short term water resources planning strategies. The applicability of K-Means cluster analysis is explored for grouping 36 GCMs from Coupled Model Intercomparison Project 5 for maximum temperature (MAXT), minimum temperature (MINT) and a combination of maximum and minimum temperature (COMBT) over India. Cluster validation methods, namely the Davies–Bouldin Index (DBI) and F-statistic, are used to obtain an optimal number of clusters of GCMs for India. The indicator chosen for evaluation of GCMs is the probability density function based skill score. It is noticed that the optimal number of clusters for MAXT, MINT and COMBT scenarios are 3, 2 and 2, respectively. Accordingly, suitable ensembles of GCMs are suggested for India for MAXT, MINT and COMBT individually. The suggested methodology can be extended to any number of GCMs and indicators, with minor modifications.

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Observation-based selection of climate models projects Arctic ice-free summers around 2035

Communications Earth & Environment ◽

10.1038/s43247-021-00214-7 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

David Docquier ◽

Torben Koenigk

Keyword(s):

Model Selection ◽

Sea Ice ◽

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Arctic Sea Ice ◽

Global Climate Models ◽

The Arctic ◽

Arctic Sea ◽

Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

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Joint Occurrence of Daily Temperature and Precipitation Extreme Events over Canada

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-13-0361.1 ◽

2014 ◽

Vol 53 (9) ◽

pp. 2148-2162 ◽

Cited By ~ 18

Author(s):

Bárbara Tencer ◽

Andrew Weaver ◽

Francis Zwiers

Keyword(s):

Climate Models ◽

Regional Climate ◽

Heavy Precipitation ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Negative Consequences ◽

Temperature And Precipitation ◽

Joint Occurrence

AbstractThe occurrence of individual extremes such as temperature and precipitation extremes can have a great impact on the environment. Agriculture, energy demands, and human health, among other activities, can be affected by extremely high or low temperatures and by extremely dry or wet conditions. The simultaneous or proximate occurrence of both types of extremes could lead to even more profound consequences, however. For example, a dry period can have more negative consequences on agriculture if it is concomitant with or followed by a period of extremely high temperatures. This study analyzes the joint occurrence of very wet conditions and high/low temperature events at stations in Canada. More than one-half of the stations showed a significant positive relationship at the daily time scale between warm nights (daily minimum temperature greater than the 90th percentile) or warm days (daily maximum temperature above the 90th percentile) and heavy-precipitation events (daily precipitation exceeding the 75th percentile), with the greater frequencies found for the east and southwest coasts during autumn and winter. Cold days (daily maximum temperature below the 10th percentile) occur together with intense precipitation more frequently during spring and summer. Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulations was used.

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Assessing the contribution of multiple forcings to changes in temperature extremes 1981–2020 using CMIP6 climate models

10.5194/egusphere-egu21-7137 ◽

2021 ◽

Author(s):

Mastawesha Misganaw Engdaw ◽

Andrew Ballinger ◽

Gabriele Hegerl ◽

Andrea Steiner

Keyword(s):

Climate Change ◽

Greenhouse Gases ◽

Climate Models ◽

Temporal Trends ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Temperature Extremes ◽

Cold Temperatures

In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981&#8211;2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.Keywords: climate change, temperature, extreme events, attribution, CMIP6&#160;Acknowledgement: This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)

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