Projections Of Climate Extremes Research Articles

Trends and projections of climate extremes in the Black Volta River Basin in West Africa

This study used the RClimDex software to examine trends in extreme air temperature and rainfall in the Black Volta River Basin (BVRB) for the present (1981–2010) and future 2051–2080 (late twenty-first century) horizons. The analysis of the future extreme events was conducted using data output of four ensemble models for two IPCC emission scenarios, Representative Concentration Pathways (RCPs) 4.5 and 8.5. A bias correction method, the quantile-quantile (Q-Q) transformation technique, was applied to all the modelled temperature and rainfall data set prior to the index calculation. The results of analysis of the present-day climate indicate warming and wetting of the BVRB. Increasing trends were seen in the extreme warm indices while the extreme cold indices showed mostly decreasing trends. Majority of the trends observed in the indices were statistically significant (95% confidence level). The extremes in rainfall also showed increasing trends in amounts and intensity of rainfall events (majority of increasing trends were statistically insignificant). Projected temperatures for the late twenty-first century showed decreasing and increasing trends in the cold and warm indices respectively, suggesting warming during the period. Trend analysis of future rainfall projections mostly showed a mix of positive and negative trends offering no clear indication of the direction of change in majority of the extreme rainfall indices. An increase in extremely wet day events is however projected for the period. The results from this study could inform climate change adaptation strategies targeted at reducing vulnerability and building resilience to extreme weather events in the BVRB.

Simulated and projected climate extremes in the Tarim River Basin using the regional climate model CCLM

The reduction of uncertainty in simulations and projections of regional climate models is a critical issue for regional climate impact studies, especially in the context of climate extremes. In this study, the regional climate model COSMO-CLM (CCLM) is evaluated in terms of daily precipitation and temperature characteristics, in order to obtain reliable projections of climate extremes for the Tarim River Basin (TRB) in Northwest China. The results show that CCLM can acceptably reproduce the annual cycle of maximum and minimum temperature, as well as the spatial distribution of precipitation pattern. Nonetheless, some systematic biases have been encountered. The equidistant cumulative distribution function matching method has been applied, which led to an efficient reduction of the systematic biases of observed and simulated climate variables. The bias correction has further been applied to climate projections for the period of 2016–2035 under the Representative Concentration Pathway 4.5. The projected indices of climate extremes as calculated from the bias-corrected CCLM projections show that most of the TRB is likely to experience a decrease in daily temperature range, and an increase in minimum temperature as well as consecutive wet days. The total precipitation on very wet days is projected to slightly increase at most stations, while the annual total precipitation will mostly increase in the southwestern parts of the TRB. The findings on the spatial–temporal patterns of these climate extremes will enable decision makers, especially in the water and agricultural sectors, to adapt and be better prepared for future climate impacts in the region.

Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

Abstract. Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems and human communities worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean–land–atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "big data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.