The Impact of the African Great Lakes on the Regional Climate

Although the African Great Lakes are important regulators for the East African climate, their influence on atmospheric dynamics and the regional hydrological cycle remains poorly understood. This study aims to assess this impact by comparing a regional climate model simulation that resolves individual lakes and explicitly computes lake temperatures to a simulation without lakes. The Consortium for Small-Scale Modelling model in climate mode (COSMO-CLM) coupled to the Freshwater Lake model (FLake) and Community Land Model (CLM) is used to dynamically downscale a simulation from the African Coordinated Regional Downscaling Experiment (CORDEX-Africa) to 7-km grid spacing for the period of 1999–2008. Evaluation of the model reveals good performance compared to both in situ and satellite observations, especially for spatiotemporal variability of lake surface temperatures (0.68-K bias), and precipitation (−116 mm yr−1 or 8% bias). Model integrations indicate that the four major African Great Lakes almost double the annual precipitation amounts over their surface but hardly exert any influence on precipitation beyond their shores. Except for Lake Kivu, the largest lakes also cool the annual near-surface air by −0.6 to −0.9 K on average, this time with pronounced downwind influence. The lake-induced cooling happens during daytime, when the lakes absorb incoming solar radiation and inhibit upward turbulent heat transport. At night, when this heat is released, the lakes warm the near-surface air. Furthermore, Lake Victoria has a profound influence on atmospheric dynamics and stability, as it induces circular airflow with over-lake convective inhibition during daytime and the reversed pattern at night. Overall, this study shows the added value of resolving individual lakes and realistically representing lake surface temperatures for climate studies in this region

Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments

Landslides and flash floods are geomorphic hazards (GHs) that often co-occur and interact. They generally occur very quickly, leading to catastrophic socioeconomic impacts. Understanding the temporal patterns of occurrence of GH events is essential for hazard assessment, early warning, and disaster risk reduction strategies. However, temporal information is often poorly constrained, especially in frequently cloud-covered tropical regions, where optical-based satellite data are insufficient. Here we present a regionally applicable methodology to accurately estimate GH event timing that requires no prior knowledge of the GH event timing, using synthetic aperture radar (SAR) remote sensing. SAR can penetrate through clouds and therefore provides an ideal tool for constraining GH event timing. We use the open-access Copernicus Sentinel-1 (S1) SAR satellite that provides global coverage, high spatial resolution (∼10–15 m), and a high repeat time (6–12 d) from 2016 to 2020. We investigate the amplitude, detrended amplitude, spatial amplitude correlation, coherence, and detrended coherence time series in their suitability to constrain GH event timing. We apply the methodology on four recent large GH events located in Uganda, Rwanda, Burundi, and the Democratic Republic of the Congo (DRC) containing a total of about 2500 manually mapped landslides and flash flood features located in several contrasting landscape types. The amplitude and detrended amplitude time series in our methodology do not prove to be effective in accurate GH event timing estimation, with estimated timing accuracies ranging from a 13 to 1000 d difference. A clear increase in accuracy is obtained from spatial amplitude correlation (SAC) with estimated timing accuracies ranging from a 1 to 85 d difference. However, the most accurate results are achieved with coherence and detrended coherence with estimated timing accuracies ranging from a 1 to 47 d difference. The amplitude time series reflect the influence of seasonal dynamics, which cause the timing estimations to be further away from the actual GH event occurrence compared to the other data products. Timing estimations are generally closer to the actual GH event occurrence for GH events within homogenous densely vegetated landscape and further for GH events within complex cultivated heterogenous landscapes. We believe that the complexity of the different contrasting landscapes we study is an added value for the transferability of the methodology, and together with the open-access and global coverage of S1 data it has the potential to be widely applicable.</p

Modelled biophysical impacts of conservation agriculture on local climates

Including the parameterization of land management practices into Earth System Models has been shown to influence the simulation of regional climates, particularly for temperature extremes. However, recent model development has focused on implementing irrigation where other land management practices such as conservation agriculture (CA) has been limited due to the lack of global spatially explicit datasets describing where this form of management is practiced. Here, we implement a representation of CA into the Community Earth System Model and show that the quality of simulated surface energy fluxes improves when including more information on how agricultural land is managed. We also compare the climate response at the subgrid scale where CA is applied. We find that CA generally contributes to local cooling (~1°C) of hot temperature extremes in mid-latitude regions where it is practiced, while over tropical locations CA contributes to local warming (~1°C) due to changes in evapotranspiration dominating the effects of enhanced surface albedo. In particular, changes in the partitioning of evapotranspiration between soil evaporation and transpiration are critical for the sign of the temperature change: a cooling occurs only when the soil moisture retention and associated enhanced transpiration is sufficient to offset the warming from reduced soil evaporation. Finally, we examine the climate change mitigation potential of CA by comparing a simulation with present-day CA extent to a simulation where CA is expanded to all suitable crop areas. Here, our results indicate that while the local temperature response to CA is considerable cooling (>2°C), the grid-scale changes in climate are counteractive due to negative atmospheric feedbacks. Overall, our results underline that CA has a nonnegligible impact on the local climate and that it should therefore be considered in future climate projections

Present‐day irrigation mitigates heat extremes

Irrigation is an essential practice for sustaining global food production and many regional economies. Emerging scientific evidence indicates that irrigation substantially affects mean climate conditions in different regions of the world. Yet how this practice influences climate extremes is currently unknown. Here we use ensemble simulations with the Community Earth System Model to assess the impacts of irrigation on climate extremes. An evaluation of the model performance reveals that irrigation has a small yet overall beneficial effect on the representation of present-day near-surface climate. While the influence of irrigation on annual mean temperatures is limited, we find a large impact on temperature extremes, with a particularly strong cooling during the hottest day of the year (−0.78 K averaged over irrigated land). The strong influence on extremes stems from the timing of irrigation and its influence on land-atmosphere coupling strength. Together these effects result in asymmetric temperature responses, with a more pronounced cooling during hot and/or dry periods. The influence of irrigation is even more pronounced when considering subgrid-scale model output, suggesting that local effects of land management are far more important than previously thought. Our results underline that irrigation has substantially reduced our exposure to hot temperature extremes in the past and highlight the need to account for irrigation in future climate projections

Implementation and Evaluation of Irrigation Techniques in the Community Land Model

Several previous studies have highlighted the irrigation-induced impacts on the global and regional water cycle, energy budget, and near-surface climate. While land models are widely used to address this question, the implementations of irrigation in these models vary in complexity. Here, we expand the representation of irrigation in Community Land Model to enable six different irrigation methods. We find that using a combination of irrigation methods, including default, sprinkler, flood and paddy techniques performs best as determined by evaluating the simulated irrigation water withdrawals against observations, and therefore select this combination as the new irrigation scheme. Then, the impact of the new irrigation scheme on surface fluxes is evaluated and detected using single-point simulations. Finally, the global and regional irrigation-induced impacts on surface energy and water fluxes are compared using both the original and the new irrigation scheme. The new irrigation scheme substantially reduces the bias and root-mean-square error of simulated irrigation water withdrawal in the USA and other countries, but considerably overestimates withdrawals in Central China. Results of single-point experiments show that different irrigation methods have different effects on surface fluxes, while the magnitudes are small. At the global scale, the new scheme enlarges the irrigation-induced impacts on water and energy variables relative to the original scheme, with varying magnitudes across regions. Overall, our results suggest that this newly developed scheme is a better tool for simulating irrigation-induced impacts on climate, and highlight the added value of incorporating human water management in Earth system models

Future intensification of precipitation and wind gust associated thunderstorms over Lake Victoria

Severe thunderstorms affect more than 30 million people living along the shores of Lake Victoria (East Africa). Thousands of fishers lose their lives on the lake every year. While deadly waves are assumed to be initiated by severe wind gusts, knowledge about thunderstorms is restricted to precipitation or environmental proxies. Here we use a regional climate model run at convection-permitting resolution to simulate both precipitation and wind gusts over Lake Victoria for a historical 10-year period. In addition, a pseudo global warming simulation provides insight into the region’s future climate. In this simulation, ERA5’s initial and boundary conditions are perturbed with atmospheric changes between 1995–2025 and 2070–2100, projected by CMIP6’s ensemble mean. It was found that future decreases in both mean precipitation and wind gusts over Lake Victoria can be attributed to a weaker mean mesoscale circulation that reduces the trigger for over-lake nighttime convection and decreases the mean wind shear. However, an intensification of extremes is projected for both over-lake precipitation and wind gusts. The observed 7 %K−1 Clausius–Clapeyron extreme precipitation scaling is ascribed to increased water vapor content and a compensation of weaker mesoscale circulations and stronger thunderstorm dynamics. More frequent wind gust extremes result from higher wind shear conditions and more compound thunderstorms with both intense rainfall and severe wind gusts. Overall, our study emphasizes Lake Victoria’s modulating role in determining regional current and future extremes, in addition to changes expected from the Clausius–Clapeyron relation

Climate change reduces winter overland travel across the Pan-Arctic even under low-end global warming scenarios

Amplified climate warming has led to permafrost degradation and a shortening of the winter season, both impacting cost-effective overland travel across the Arctic. Here we use, for the first time, four state-of-the-art Land Surface Models that explicitly consider ground freezing states, forced by a subset of bias-adjusted CMIP5 General Circulation Models to estimate the impact of different global warming scenarios (RCP2.6, 6.0, 8.5) on two modes of winter travel: overland travel days (OTDs) and ice road construction days (IRCDs). We show that OTDs decrease by on average −13% in the near future (2021–2050) and between −15% (RCP2.6) and −40% (RCP8.5) in the far future (2070–2099) compared to the reference period (1971–2000) when 173 d yr−1 are simulated across the Pan-Arctic. Regionally, we identified Eastern Siberia (Sakha (Yakutia), Khabarovsk Krai, Magadan Oblast) to be most resilient to climate change, while Alaska (USA), the Northwestern Russian regions (Yamalo, Arkhangelsk Oblast, Nenets, Komi, Khanty-Mansiy), Northern Europe and Chukotka are highly vulnerable. The change in OTDs is most pronounced during the shoulder season, particularly in autumn. The IRCDs reduce on average twice as much as the OTDs under all climate scenarios resulting in shorter operational duration. The results of the low-end global warming scenario (RCP2.6) emphasize that stringent climate mitigation policies have the potential to reduce the impact of climate change on winter mobility in the second half of the 21st century. Nevertheless, even under RCP2.6, our results suggest substantially reduced winter overland travel implying a severe threat to livelihoods of remote communities and increasing costs for resource exploration and transport across the Arctic

Phytoplankton pigment analysis as a tool for monitoring a tropical great lake, Lake Kivu (East Africa)

Lake Kivu, East Africa, is a deep oligotrophic and meromictic lake containing high amounts of dissolved methane (∼55–60 km3) and carbon dioxide (∼300 km3) in its deep waters. Methane harvesting for energy production began in 2015, and a monitoring programme was set up to assess possible disturbance on the ecosystem. Phytoplankton biomass and composition was assessed twice per month or monthly at 2 monitoring sites between June 2005 and December 2019, based on HPLC analysis of chlorophyll a (Chl-a) and marker pigments. This long-term series shows that significant changes occurred around 2010 in the lake phytoplankton, with a notable increase of Chl-a and changes in the assemblage toward an increase in non-motile green algae and diatoms. To assess possible changes due to methane harvesting, we compared 2 periods, 2012–2014 and 2018–2019. Chl-a concentration decreased slightly in 2018–2019 compared to the reference period of 2012–2014, and significant changes occurred in composition of the phytoplankton assemblage. In terms of relative contribution to Chl-a, diatoms increased from 26% to 46%, whereas green algae decreased ∼2-fold, from 35% in 2012–2014 to 18% in 2018–2019. Multivariate analyses showed that phytoplankton composition was influenced by seasonal and interannual variations of limnological variables related to changes in meteorological factors. To assess possible future changes due to methane exploitation, we recommend increasing sampling frequency and taxonomic resolution, as well as improving environmental data acquisition

Combined microsatellite and FGFR3 mutation analysis enables a highly sensitive detection of urothelial cell carcinoma in voided urine

PURPOSE: Fibroblast growth factor receptor 3 (FGFR3) mutations were reported recently at a high frequency in low-grade urothelial cell carcinoma (UCC). We investigated the feasibility of combining microsatellite analysis (MA) and the FGFR3 status for the detection of UCC in voided urine. EXPERIMENTAL DESIGN: In a prospective setting, 59 UCC tissues and matched urine samples were obtained, and subjected to MA (23 markers) and FGFR3 mutation analysis (exons 7, 10, and 15). In each case, a clinical record with tumor and urine features was provided. Fifteen patients with a negative cystoscopy during follow-up served as controls. RESULTS: A mutation in the FGFR3 gene was found in 26 (44%) UCCs of which 22 concerned solitary pTaG1/2 lesions. These mutations were absent in the 15 G3 tumors. For the 6 cases with leukocyturia, 46 microsatellite alterations were found in the tumor. Only 1 of these was also detected in the urine. This was 125 of 357 for the 53 cases without leukocyte contamination. The sensitivity of MA on voided urine was lower for FGFR3-positive UCC (15 of 21; 71%) as compared with FGFR3 wild-type UCC (29 of 32; 91%). By including the FGFR3 mutation, the sensitivity of molecular cytology increased to 89% and was superior to the sensitivity of morphological cytology (25%) for every clinical subdivision. The specificity was 14 of 15 (93%) for the two (molecular and morphological) cytological approaches. CONCLUSIONS: Molecular urine cytology by MA and FGFR3 mutation analysis enables a highly sensitive and specific detection of UCC. The similarity of molecular profiles in tumor and urine corroborate their clonal relation