Time Scales and Sources of European Temperature Variability

Skillful predictions of continental climate would be of great practical benefit for society and stakeholders. It nevertheless remains fundamentally unresolved to what extent climate is predictable, for what features, at what time scales, and by which mechanisms. Here we identify the dominant time scales and sources of European surface air temperature (SAT) variability during the cold season using a coupled climate reanalysis, and a statistical method that estimates SAT variability due to atmospheric circulation anomalies. We find that eastern Europe is dominated by subdecadal SAT variability associated with the North Atlantic Oscillation, whereas interdecadal and multidecadal SAT variability over northern and southern Europe are thermodynamically driven by ocean temperature anomalies. Our results provide evidence that temperature anomalies in the North Atlantic Ocean are advected over land by the mean westerly winds and, hence, provide a mechanism through which ocean temperature controls the variability and provides predictability of European SAT.publishedVersio

Downscaling an intense precipitation event in complex terrain: the importance of high grid resolution

Floods due to intense rainfall are a major hazard to both people and infrastructure in western Norway. Here steep orography enhances precipitation and the complex terrain channels the runoff into narrow valleys and small rivers. In this study we investigate a major rainfall and flooding event in October 2014. We compare high-resolution numerical simulations with measurements from rain gauges deployed in the impacted region. Our study has two objectives: (i) to understand the dynamical processes that drove the high rainfall and (ii) the importance of high grid resolution to resolve intense rainfall in complex terrain. This is of great interest for numerical weather prediction and hydrological modelling. Our approach is to dynamically downscale the ERA-Interim reanalysis with the Weather Research and Forecasting model (WRF). We find that WRF gives a substantially better representation of precipitation both in terms of absolute values as well as spatial and temporal distributions than a coarse resolution reanalysis. The largest improvement between the WRF simulations is found when we decrease the horizontal model grid spacing from 9 km to 3 km. Only minor additional improvements are obtained when downscaling further to 1 km. We believe that this is mainly related to the orography in the study area and its representation in the model. Realistic representations of gravity waves and the seeder–feeder effect seem to play crucial roles in reproducing the precipitation distribution correctly. An analysis of associated wavelengths shows the importance of the shortest resolvable length scales. On these scales our simulations also show differences in accumulated precipitation of up to 300 mm over four days, further emphasising the need for resolving short wavelengths. Therefore, our results clearly demonstrate the need for high-resolution dynamical downscaling for extreme weather impact studies in regions with complex terrain.publishedVersio

Lagged oceanic effects on the East African short rains

The East African ‘short rains’ in October–December (OND) exhibit large interannual variability. Drought and flooding are not unusual, and long-range rainfall forecasts can guide planning and preparedness for such events. Although seasonal forecasts based on dynamical models are making inroads, statistical models based on sea surface temperature (SST) precursors are still widely used, making it important to better understand the strengths and weaknesses of such models. Here we define a simple statistical forecast model, which is used as a tool to shed light on the dynamics that link SSTs and rainfall across time and space, as well as on why such models sometimes fail. Our model is a linear regression, where the August states of El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) predict about 40% of the short rains variability in 1950–2020. The forecast errors are traced back to the initial SSTs: too-wet (too-dry) forecasts are linked linearly to positive (negative) initial ENSO and IOD states in August. The link to the initial IOD state is mediated by changes in the IOD between August and OND, highlighting a physical mechanism for prediction busts. We also identify asymmetry and nonlinearity: when ENSO and/or the IOD are positive in August, the range and variance of OND forecast errors are larger than when the SST indices are negative. Upfront adjustments of predictions conditional on initial SSTs would have helped in some years with large forecast busts, such as the dry 1987 season during a major El Niño, for which the model erroneously predicts copious rainfall, but it would have exacerbated the forecast in the wet 2019 season, when the IOD was strongly positive and the model predicts too-dry conditions.publishedVersio

Lagged oceanic effects on the East African short rains

The East African ‘short rains’ in October–December (OND) exhibit large interannual variability. Drought and flooding are not unusual, and long-range rainfall forecasts can guide planning and preparedness for such events. Although seasonal forecasts based on dynamical models are making inroads, statistical models based on sea surface temperature (SST) precursors are still widely used, making it important to better understand the strengths and weaknesses of such models. Here we define a simple statistical forecast model, which is used as a tool to shed light on the dynamics that link SSTs and rainfall across time and space, as well as on why such models sometimes fail. Our model is a linear regression, where the August states of El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) predict about 40% of the short rains variability in 1950–2020. The forecast errors are traced back to the initial SSTs: too-wet (too-dry) forecasts are linked linearly to positive (negative) initial ENSO and IOD states in August. The link to the initial IOD state is mediated by changes in the IOD between August and OND, highlighting a physical mechanism for prediction busts. We also identify asymmetry and nonlinearity: when ENSO and/or the IOD are positive in August, the range and variance of OND forecast errors are larger than when the SST indices are negative. Upfront adjustments of predictions conditional on initial SSTs would have helped in some years with large forecast busts, such as the dry 1987 season during a major El Niño, for which the model erroneously predicts copious rainfall, but it would have exacerbated the forecast in the wet 2019 season, when the IOD was strongly positive and the model predicts too-dry conditions

Effects of the Congo Basin Rainforest on Rainfall Patterns

Large-scale deforestation in the Congo Basin has an impact on rainfall patterns, both in the Basin and beyond. Factors like socio-economic drivers contribute to ongoing deforestation, and forest loss rates are expected to increase. The mechanisms linking deforestation and rainfall are complex. On a local scale, deforested areas might experience increased rainfall, but adjacent forests could see reduced rainfall. On larger scales, widespread deforestation can reduce overall rainfall in large areas. These changes can impact agriculture, with delayed rainfall and shorter rainy seasons affecting crop yields. By 2100, projected forest loss in the Congo Basin may reduce annual rainfall by 8-10%. However, uncertainties remain due to limited data and understanding of rainfall drivers and interactions in the region.publishedVersio

Drivers of Subseasonal Forecast Errors of the East African Short Rains

The ‘short rains’ in East Africa from October to December have significant year-to-year variability. Their abundance or deficiency is often associated with floods or droughts for which early warning is crucial, though even in normal seasons skillful forecasts facilitate planning and preparedness. Here we study the relationship between initial-state sea surface temperatures and subseasonal rainfall forecast errors in the European Center for Medium-Range Weather Forecasts model in the region. We demonstrate that the initial mode of the Indian Ocean Dipole (IOD) is a partial control on the rainfall error in weeks 3–4. This relationship is also clear on the seasonal scale, exemplified by too-wet forecasts during the 2015 season when the IOD was positive, and too-dry forecasts in 2010 when it was negative. Our results provide an entry point for model improvement, and we show that a priori forecast corrections based on the initial IOD index are feasible

Downscaling an intense precipitation event in complex terrain: the importance of high grid resolution

Floods due to intense rainfall are a major hazard to both people and infrastructure in western Norway. Here steep orography enhances precipitation and the complex terrain channels the runoff into narrow valleys and small rivers. In this study we investigate a major rainfall and flooding event in October 2014. We compare high-resolution numerical simulations with measurements from rain gauges deployed in the impacted region. Our study has two objectives: (i) to understand the dynamical processes that drove the high rainfall and (ii) the importance of high grid resolution to resolve intense rainfall in complex terrain. This is of great interest for numerical weather prediction and hydrological modelling. Our approach is to dynamically downscale the ERA-Interim reanalysis with the Weather Research and Forecasting model (WRF). We find that WRF gives a substantially better representation of precipitation both in terms of absolute values as well as spatial and temporal distributions than a coarse resolution reanalysis. The largest improvement between the WRF simulations is found when we decrease the horizontal model grid spacing from 9 km to 3 km. Only minor additional improvements are obtained when downscaling further to 1 km. We believe that this is mainly related to the orography in the study area and its representation in the model. Realistic representations of gravity waves and the seeder–feeder effect seem to play crucial roles in reproducing the precipitation distribution correctly. An analysis of associated wavelengths shows the importance of the shortest resolvable length scales. On these scales our simulations also show differences in accumulated precipitation of up to 300 mm over four days, further emphasising the need for resolving short wavelengths. Therefore, our results clearly demonstrate the need for high-resolution dynamical downscaling for extreme weather impact studies in regions with complex terrain

Diverse Surface Signatures of Stratospheric Polar Vortex Anomalies

The Arctic stratospheric polar vortex is an important driver of winter weather and climate variability and predictability in North America and Eurasia, with a downward influence that on average projects onto the North Atlantic Oscillation (NAO). While tropospheric circulation anomalies accompanying anomalous vortex states display substantial case-by-case variability, understanding the full diversity of the surface signatures requires larger sample sizes than those available from reanalyses. Here, we first show that a large ensemble of seasonal hindcasts realistically reproduces the observed average surface signatures for weak and strong vortex winters and produces sufficient spread for single ensemble members to be considered as alternative realizations. We then use the ensemble to analyze the diversity of surface signatures during weak and strong vortex winters. Over Eurasia, relatively few weak vortex winters are associated with large-scale cold conditions, suggesting that the strength of the observed cold signature could be inflated due to insufficient sampling. For both weak and strong vortex winters, the canonical temperature pattern in Eurasia only clearly arises when North Atlantic sea surface temperatures are in phase with the NAO. Over North America, while the main driver of interannual winter temperature variability is the El Niño–Southern Oscillation (ENSO), the stratosphere can modulate ENSO teleconnections, affecting temperature and circulation anomalies over North America and downstream. These findings confirm that anomalous vortex states are associated with a broad spectrum of surface climate anomalies on the seasonal scale, which may not be fully captured by the small observational sample size.publishedVersio

Large‐scale regional model biases in the extra‐tropical North Atlantic storm track and impacts on downstream precipitation

Global climate models have circulation biases that the community aims to reduce, for instance through high‐resolution dynamical downscaling. We used the Weather Research and Forecasting model (WRF) to downscale both ERA‐Interim and a bias‐corrected version of the Norwegian climate model NorESM1‐M on a high‐resolution grid. By varying the domain size, we investigated the influence of the driving data and highly resolved topography on the North Atlantic storm track and the precipitation in its exit region. In our largest domains, we found large‐scale circulation and storm track biases similar to those seen in global models and with spatial patterns independent of the driving data. The biases in the smaller domains were more dependent on the quality of the driving data. Nevertheless, the biases had little effect on the simulated precipitation in Norway. Although the added value of downscaling was clear with respect to the global climate models, all the downscaled simulations showed similar precipitation frequencies and intensities. We posit that, because the precipitation is so strongly governed by the local topographic forcing, a correct storm track is less critical for the precipitation distribution