Search

Around 80 % of global soybean supply is produced in southeast South America (SESA), central Brazil (CB) and the United States (US) alone. This concentration of production in few regions makes global soybean supply sensitive to spatially compounding harvest failures. Weather variability is a key driver of soybean variability, with soybeans being especially vulnerable to hot and dry conditions during the reproductive growth stage in summer. El Niño–Southern Oscillation (ENSO) teleconnections can influence summer weather conditions across the Americas, presenting potential risks for spatially compounding harvest failures. Here, we develop causal structural models to quantify the influence of ENSO on soybean yields via mediating variables like local weather conditions and extratropical sea surface temperatures (SSTs). We show that soybean yields are predominately driven by soil moisture conditions in summer, explaining ∼50 %, 18 % and 40 % of yield variability in SESA, CB and the US respectively. Summer soil moisture is strongly driven by spring soil moisture, as well as by remote extratropical SST patterns in both hemispheres. Both of these soil moisture drivers are again influenced by ENSO. Our causal models show that persistent negative ENSO anomalies of −1.5 standard deviation (SD) lead to a −0.4 SD soybean reduction in the US and SESA. When spring soil moisture and extratropical SST precursors are pronouncedly negative (−1.5 SD), then estimated soybean losses increase to −0.9 SD for the US and SESA. Thus, by influencing extratropical SSTs and spring soil moisture, persistent La Niñas can trigger substantial soybean losses in both the US and SESA, with only minor potential gains in CB. Our findings highlight the physical pathways by which ENSO conditions can drive spatially compounding events. Such information may increase preparedness against climate-related global soybean supply shocks.

Soy harvest failure events can severely impact farmers, insurance companies and raise global prices. Reliable seasonal forecasts of mis-harvests would allow stakeholders to prepare and take appropriate early action. However, especially for farmers, the reliability and lead-time of current prediction systems provide insufficient information to justify within-season adaptation measures. Recent innovations increased our ability to generate reliable statistical seasonal forecasts. Here, we combine these innovations to predict the 1-3 poor soy harvest years in eastern US. We first use a clustering algorithm to spatially aggregate crop producing regions within the eastern US that are particularly sensitive to hot-dry weather conditions. Next, we use observational climate variables (sea surface temperature (SST) and soil moisture) to extract precursor timeseries at multiple lags. This allows the machine learning model to learn the low-frequency evolution, which carries important information for predictability. A selection based on causal inference allows for physically interpretable precursors. We show that the robust selected predictors are associated with the evolution of the horseshoe Pacific SST pattern, in line with previous research. We use the state of the horseshoe Pacific to identify years with enhanced predictability. We achieve very high forecast skill of poor harvests events, even 3 months prior to sowing, using a strict one-step-ahead train-test splitting. Over the last 25 years, 82% of the in February predicted poor harvests were correct. When operational, this forecast would enable farmers (and insurance/trading companies) to make informed decisions on adaption measures, e.g., selecting more drought-resistant cultivars, invest in insurance, change planting management.

Reliable (sub) seasonal (S2S) forecasts remain a huge scientific challenge. The lead-time is too long to benefit from the atmosphere’s inertial memory, but too short for the atmosphere’s boundary conditions to be felt strongly. Only for specific "windows of predictability" (i.e. specific regions, timescales and climatic background states), skillful forecasts are possible, in an otherwise largely unpredictable future. Due to a number of successes in S2S forecasting, the interest in machine learning (ML) is growing fast. However, we argue there is a need for more standardization, consensus on best practices, higher efficiency, and higher reproducibility. Typical S2S ML use-cases, such as (1) pure statistical forecasting based on observations, (2) transfer learning, and (3) post-processing of dynamical model ensembles, require a large coding and preprocessing effort. Such experiments are not trivial to set up, and without sufficient experience and expertise there is a large risk of improper cross-validation and/or improper and non-standard verification.Within a 3-year project, we are developing a high-level Python package called s2spy. Our aim is to make ML workflows more transparent and easier to build, and to facilitate standardization and collaboration across the S2S community. s2spy also contributes to a higher reproducibility and works towards a wider acceptance of standards and best practices. We will present our vision and the capabilities of our package, show-casing that we can build a model from raw climate data up to verification and explanation in only a few lines of code.

Many factors have been suggested to explain variability and change in Sahel rainfall. Of those, sea surface temperature (SST) in the Eastern Mediterranean Sea (EMS) and zonal moisture flux south of the Sahel show strong correlations. Based on observational and reanalysis data on temperature, pressure, wind and moisture flux, this paper identifies a mechanism that explains both correlations. The mechanism hinges on the Jebel Marra massif and the Ethiopian highlands, where the mesoscale convective systems (MCSs) develop that bring most of the rain to the Sahel. We find that cold SST anomalies in the EMS between June and September cause a greater trans-Sahara temperature contrast and coincide with high pressure over Libya, resulting in stronger northerlies towards Sudan. This prevents Tropical Atlantic moisture from reaching the MCS genesis region, which reduces the seasonal northward spread of Sahel rainfall and of the Atlantic intertropical convergence zone, which in turn suppresses the development of the west-African westerly jet and the African westerly jet and inhibits Atlantic moisture from reaching the MCS genesis region, thus further reducing Sahel rainfall. Anomalous moisture transport from the Mediterranean does not play a role. Mediterranean SST variability raises questions about the future development of Sahel rainfall. If a new dry period materialises, this will have substantial implications on food production in the region. There are however opportunities for mitigating against the effects of such a dry period.

Persistent hot–dry or cold–wet summer weather can have significant impacts on agriculture, health, and the environment. For northwestern Europe, these weather regimes are typically linked to, respectively, blocked or zonal jet stream states. The fundamental dynamics underlying these circulation states are still poorly understood. Edward Lorenz postulated that summer circulation may be either fully or almost intransitive, implying that part of the phase space (capturing circulation variability) cannot be reached within one specific summer. If true, this would have major implications for the predictability of summer weather and our understanding of the drivers of interannual variability of summer weather. Here, we test the two Lorenz hypotheses (i.e., fully or almost intransitive) for European summer circulation, capitalizing on a newly available very large ensemble (2000 years) of present-day climate data in the fully coupled global climate model EC-Earth. Using self-organizing maps, we quantify the phase space of summer circulation and the trajectories through phase space in unprecedented detail. We show that, based on Markov assumptions, the summer circulation is strongly dependent on its initial state in early summer with the atmospheric memory ranging from 28 days up to ~45 days. The memory is particularly long if the initial state is either a blocked or a zonal flow state. Furthermore, we identify two groups of summers that are characterized by distinctly different trajectories through phase space, and that prefer either a blocked or zonal circulation state, respectively. These results suggest that intransitivity is indeed a fundamental property of the atmosphere and an important driver of interannual variability.

Global-scale af-/reforestation (A/R) and deforestation substantially changes the Earth’s energy and water fluxes, thereby affecting the large-scale atmospheric circulation and thus have significant impacts on weather systems. During summer, A/R and deforestation induced changes in the soil moisture are shown to have an impact on the planetary wave response through the jet stream. Such changes might lead to high-amplitude, quasi-stationary circumglobal Rossby waves that have been associated with extreme summer heatwaves and persistent high-impact extremes. In this study we investigate how idealized global land use and land management changes can alter the boreal summer circulation with a focus on the response of the jet stream. For the analysis we conducted model experiments with three fully coupled Earth System Models (EC-EARTH, MPI-ESM and CESM). Each scenario run for 160 years from which we analyze the final 30 years. A control run with constant current land use and land management is compared to a global A/R and a global deforestation (global cropland expansion) simulation. In order to assess clean land-atmosphere interactions, all simulations are kept with constant present-day atmospheric forcings (year 2014). We investigate the potential changes in the amplitude of the waves, the likelihood of quasi-stationary wave activity, and of summer blockings within the three different simulations, and the weather consequences that such changes lead to.

High-amplitude quasi-stationary atmospheric Rossby waves with zonal wave numbers 6 to 8 associated with the phenomenon of quasi-resonant amplification (QRA) have been linked to persistent summer extreme weather events in the Northern Hemisphere. We project future occurrence of QRA events based on an index derived from the zonally averaged surface temperature field, comparing results from CMIP5 and CMIP6 (Coupled Model Intercomparison Projects) climate projections. Under the scenarios analyzed, there is a general agreement among models, with most simulations projecting a substantial increase in QRA index. Larger increases are found among CMIP6-SSP585 (42 models, 46 realizations) models with 85% of models displaying a positive trend, as compared with as compared with 60% of CMIP5-RCP85 (35 models, 75 realizations), and a reduced spread among SSP585 models. The CMIP6-SSP370 (24 models, 28 realizations) simulations display qualitatively similar behavior to SSP585, indicating a substantial increase in QRA events under business-as-usual emissions scenarios. Our analysis suggests that anthropogenic warming will likely lead to an even more substantial increase in QRA events (and associated summer weather extremes) than our previous analysis of CMIP5 simulations.

Land cover and land management changes (LCLMC) have often been highlighted as crucial regarding climate change, both for mitigation (e.g. afforestation) and adaptation (e.g. irrigation). In order to understand this role we present fully coupled Earth System Model (ESM) simulations using external forcing conditions from the SSP1-1.9 scenario, except for land cover and land management scenarios that follow differing trajectories. First we conduct a short 30-year historical simulation (histCTL) and a future (years 2015-2100) simulation under SSP1-1.9 conditions but with present day land cover kept at constant end of 2014 conditions (futCTL). These allow us to isolate climate changes in response to the SSP1-19 forcing, but in the absence of land cover changes. Secondly we conduct two simulations under SSP1-1.9 forcing, but with land cover and land management following two different trajectories. These trajectories are derived from the scenarios presented in Humpenöder et al. (2022) and represent two strongly diverging worlds with regard to socio-economic development, environmental protection, and land-based mitigation: (i) the future sustainability scenario (futSust) in which the land sector experiences sustainable development and application of mitigation strategies (such as greenhouse gas emission pricing) in all countries, (ii) the future inequality scenario (futIneq) in which these developments mostly happen in OECD countries, with the rest of the world continuing on current trends (including massive tropical deforestation). Each of these simulations have been run with three different ESMs (CESM, MPI-ESM and EC-EARTH) in order to identify how robust these results are over different ESMs.The results of these simulations can be used to increase our understanding of the role of land cover scenarios within a low-warming future as prescribed by the Paris agreement. We can compare the effects of all other forcings (futCTL- histCTL; CO2, aerosols etc.) to the effects of land cover changes in the different scenarios (futSust – futCTL or futIneq-futCTL) as well as to the difference between the future sustainability and the inequality narratives (futSust-futIneq). These results will be analysed for temperature and moisture fluxes, mainly focusing on warm and dry extremes and how land cover scenarios affect these. ReferencesHumpenöder, F., Popp, A., Schleussner, C. F., Orlov, A., Windisch, M. G., Menke, I., Pongratz, J., Havermann, F., Thiery, W., Luo, F., Jeetze, P. V., Philipp Dietrich, J., Lotze-Campen, H., Weindl, I. & Lejeune, Q. (2022). Overcoming global inequality is critical for land-based mitigation in line with the Paris Agreement. Nature Communications, 13(1), 1-15.

Recent work has shown that (sub-)seasonal variability in tropical Pacific convection, closely linked to ENSO, relates to summertime circulation over the Euro-Atlantic. The teleconnection is non-stationary, probably due to long-term changes in both the tropical Pacific and extra-tropical Atlantic. It also appears imperfectly captured by numerical models. In a previous study we found that the best predictor of errors in sub-seasonal forecasts of European temperature, is a dipole in tropical west Pacific sea surface temperatures (SSTs). In this diagnostic study we use reanalysis data to further investigate the teleconnection pathway and the processes behind its non-stationarity. We show that SST gradients associated with the dipole represent a combination of ENSO variability and west Pacific warming, and have become stronger since 1980. Associated patterns of suppressed and enhanced tropical heating are followed by quasi-stationary waves that linger for multiple weeks. Situations with La Niña-like gradients are followed by high pressure centers over eastern Europe and Russia, three to six weeks later. Inverted situations are followed by high pressure over western Europe, three to six weeks later. The latter situation is however also conditional on a strong meridional tripole in north Atlantic SST and a co-located jet stream. Overall, the sub-seasonal pathway diagnosed in this study connects to patterns detected at seasonal scales, and confirms earlier findings that the summertime connectivity between the Pacific and Europe has shifted in recent decades. It also partly explains the increased occurrence of high sea level pressures and summer temperatures over the European continent.

Over the last 70 years, extreme heat has been increasing at global scale [1,2], with a rapid rate in several regions including Western Europe [3]. Climate models broadly capture heat trends globally [1], but exhibit systematically weaker extreme heat trends than observations in Western Europe [4-6], together with a weaker summer warming [7,8]. The causes of this mismatch, confirmed here by the analysis of 273 latest generation coupled climate simulations, among which only a handful of them overpass observed trends, are not well understood. Here we use a circulation analogue approach [9,10] to identify the dynamical contribution to temperature trends [11-12], and show that a substantial fraction (1.0°C [0.4°-1.6°C] of 3.4°C per global warming degree) of the trend is due to circulation changes, largely due to increases in southerly flows over Western Europe. Their rapid increase in frequency (+63% [20%-106%] since 1950) and persistence (+45% [12%-77%]) are not captured by any of the 32 climate model flow simulations analyzed. The few simulations reaching the observed warming trends in extreme heat have weak dynamical changes, with a decrease in occurrence of southerly flows, indicating that they capture the warming trend for the wrong reasons. These model biases in circulation trends can be due to a systematically underestimated or erroneous representation of the circulation response to external forcing, or to a systematic underestimation of interdecadal variability, or both. The former implies that projections are too conservative, the latter that we are left with deep uncertainty regarding the pace of future summer heat in Europe: the current strong recent trend could weaken or increase in future decades. This calls for caution when interpreting climate projections of heat extremes over Western Europe, in particular in view of adaptation to heat waves.

The Last Interglacial (LIG; ca. 125 ka) is a period of interest for climate research as it is the most recent period of the Earth's history when the boreal climate was warmer than at present. Previous research, based on models and geological evidence, suggests that the LIG may have featured enhanced patterns of ocean storminess, but this remains hotly debated. Here, we apply state-of-the-art climate and hydrodynamic modeling to simulate changes in sea level extremes caused by storm surges, under LIG and pre-industrial climate forcings. Significantly higher seasonal LIG sea level extremes emerge for coastlines along northern Australia, the Indonesian archipelago, much of northern and eastern Africa, the Mediterranean Sea, the Gulf of Saint Lawrence, the Arabian Sea, the east coast of North America, and islands of the Pacific Ocean and of the Caribbean. Lower seasonal LIG sea level extremes emerge for coastlines along the North Sea, the Bay of Bengal, China, Vietnam, and parts of Central America. Most of these anomalies are associated with anomalies in seasonal sea level pressure minima and in eddy kinetic energy calculated from near-surface wind fields, and therefore seem to originate from anomalies in the meridional position and intensity of the predominant wind bands. In a qualitative comparison, LIG sea level extremes seem generally higher than those projected for future warmer climates. These results help to constrain the interpretation of coastal archives of LIG sea level indicators.

Around 80% of global soybean supply is produced in southeast South America (SESA), central Brazil (CB) and the United States (US) alone. This concentration of production in few regions makes global soybean supply sensitive to spatially compounding harvest failures. Weather variability is a key driver of soybean yield variability, with soybean especially vulnerable to hot and dry conditions during the reproductive growth stage in summer. El Niño Southern Oscillation (ENSO) teleconnections can influence summer weather conditions across the Americas presenting potential risks for spatially compounding harvest failures. Here, we develop causal structural models to quantify the influence of ENSO on crop yields via mediating variables like local weather conditions and extratropical sea-surface temperatures (SST). We show that soybean yields are predominately driven by soil moisture conditions in summer explaining ~50 %, 18 % and 40 % of yield variability in SESA, CB and US respectively. Summer soil moisture is strongly driven by spring soil moisture as well as remote extra-tropical SST patterns in both hemispheres. Both of these soil moisture drivers are again influenced by ENSO. Our causal models show that persistent negative ENSO anomalies of -1.5 standard deviation (SD) lead to a -0.4 SD soybean reductions in the US and SESA. When spring soil moisture and extratropical SST precursors are pronouncedly negative (-1.5 SD), then estimated soybean losses increase to -0.9 SD for US and SESA. Thus, by influencing extratropical SSTs and spring soil moisture, persistent La Niña’s can trigger substantial soybean losses in both the US and SESA, with only minor potential gains in CB. Our findings highlight the physical pathways by which ENSO conditions can drive spatially compounding events. Such information may increase preparedness against climate related global soybean supply shocks.

In recent years, East Africa (EA) has been affected by numerous drought events. To better understand the drought hazard and its implications, it is important to analyze the propagation of dry conditions through the hydrological system and identify at what point it leads to negative impacts on society. This way, we define drought events as dry conditions during which actual impacts are felt by communities (i.e. ‘impactful droughts’) rather than using a predefined relative threshold. This study explores the link between standardized and threshold-based drought indices and drought impacts in EA. Drought propagation from meteorological to soil moisture to hydrological drought to finally socio-economic impacts has been investigated in the data-rich regions around the world (i.e. Europe). However, not much has been done in data-poor regions (i.e. Africa), with very different climatic and socio-economic characteristics.We identify meaningful indicators for region- and sector-specific impact occurrences, focusing on three impact types: crop yield and livestock losses, changes in pasture represented by Normalized Drought Vegetative Index (NDVI), and people affected. In this study, we link these impact types to various drought hazard indices calculated from time series of precipitation, evaporation, soil moisture and discharge for every administrative unit in Kenya, Ethiopia and Somalia. The standardized indices are used with varying accumulation periods (1,3,6,12,24,48 months) to find the best predictors. We use independent but complementary machine learning techniques (correlation, logistic regression and random forest modelling) to identify which indices link best to which drought impacts.Using the variable threshold based analysis to analyse drought propagation in Isiolo county in Kenya, we find an increase in frequency and duration of hydrological droughts. This has been confirmed in other studies. The correlations and relationships between crop yield anomalies and indices are different in each of the administrative units. The findings in this research will lead to a greater understanding of the effects of drought hazards to communities, and contribute to improved drought monitoring and forecasting. This research can also form the basis for follow up work to explore the effects of adaptation options (i.e. irrigation) on drought hazard and impacts in East Africa.

Much of the forecast skill in the mid-latitudes on seasonal timescales originates from deep convection in the tropical belt. For boreal summer, such tropical-extratropical teleconnections are less well understood as compared to winter. Here we validate the representation of boreal tropical – extratropical teleconnections in a general circulation model in comparison with observational data. To characterise variability between tropical convective activity and mid-latitude circulation, we identify the South Asian monsoon (SAM) – circumglobal teleconnection (CGT) pattern and the western North Pacific summer monsoon (WNPSM) – North Pacific high (NPH) pairs as the leading modes of tropical-extratropical coupled variability in both reanalysis (ERA5) and seasonal forecast (SEAS5) data. We calculate causal maps, an application of the PCMCI causal discovery algorithm which identifies causal links in a 2D field, to show the causal effect of each of these patterns on circulation and convection in the Northern Hemisphere. The spatial patterns and signs of the causal links in SEAS5 closely resemble those seen in ERA5, independent of the initialization date of SEAS5. However, the strength of causal links in SEAS5 is often too weak (about two thirds of those in ERA5). By performing a subsampling (over time) experiment, we identify those regions for which SEAS5 data well reproduce ERA5 values, e.g. South-eastern US, and highlight those where the bias is more prominent, e.g. North Africa. We demonstrate that different El Niño – Southern Oscillation phases have only a marginal effect on the strength of these links. Finally, we discuss the potential role of model mean-state biases in explaining differences between SEAS5 and ERA5 causal links.

Changes in atmospheric circulation under increasing greenhouse gas concentrations are important because of their implications for weather extremes and associated societal risks. However, uncertainties in models and future projections are still large and drivers behind circulation changes are not well understood. Particularly for Europe, a potential weakening of the Atlantic meridional overturning circulation (AMOC) is considered important as it affects SST patterns and ocean–atmosphere heat fluxes and, subsequently, European climate. Here we detect and characterize changes in atmospheric circulation patterns over the North Atlantic under increasing CO2 concentrations in simulations of a very high-resolution, fully coupled climate model (CM2.6) with a realistic representation of the AMOC. We use an objective clustering technique (self-organizing maps) and validate the model’s clusters against reanalysis data. We compare the frequency of those patterns in a CO2 doubling experiment, characterized by an AMOC decline, with those in a preindustrial run, and find statistically significant changes. The most robust findings are 1) a ~30% increase in zonal flow regimes in February, relevant for flood risk in northwestern Europe, and 2) a ~60% increase in anticyclonic (high pressure) circulation directly west of the United Kingdom in August, relevant for western and central European drought. A robust decrease in the frequency of Scandinavian blocking is also seen across most months and seasons. Despite the uncertainties regarding atmospheric circulation response to climate change, our findings contribute to the increasing evidence for the emergence of robust high-impact changes over Europe.

Land management and anthropogenic land cover change (LMLCC) plays a key role in the global carbon budget. For example, approximately half of the terrestrial biomass has been removed by LMLCC to date. Conversely, large potentials for carbon dioxide removal are invoked when vegetation-based negative emission technologies such as afforestation are discussed. Previous studies on LMLCC effects on the carbon cycle focused on the direct effect of tree removal or regrowth on carbon fluxes. However, a suite of studies has shown that LMLCC has an important influence on climate via biogeophysical effects through changes in energy and water fluxes. This influence can reach far beyond the location of LMLCC, called the "nonlocal effect" of LMLCC on climate. This raises the question if LMLCC can also have non-negligible effects on the carbon cycle remote from the LMLCC location itself. Our study establishes the concept how to investigate strength and patterns of the unintended nonlocal side-effects of LMLCC on carbon stocks and fluxes.Therefore, we conducted three different fully-coupled atmosphere-ocean-land experiments of idealized global cropland expansion with and without cropland irrigation as well as global re-/afforestation starting from today's state over a 150-year period under present day solar and trace gas forcing. All experiments were simulated by three different earth system models (MPI-ESM, EC-EARTH and CESM) to additionally quantify inter-model uncertainty and potentially uncover specific model biases. Here only CESM and MPI-ESM results are presented. To separate the local and nonlocal effects we use a checkerboard approach of grid boxes with and without LMLCC as proposed by Winckler et al., 2017. That is, we separate the carbon stock changes due to LMLCC at the location of LMLCC (local effect) from those induced by climate change caused by remote LMLCC (nonlocal effect). The total effect is the sum of both, the local and nonlocal effect.The results of MPI-ESM (CESM) show that the global nonlocal effect on vegetation carbon (cVeg) accounts for 6% (3%) and 4% (0.6%) of total cVeg changes for crop expansion and afforestation simulation, respectively. Additionally, applying irrigation to crop expansion strongly increases the nonlocal climate induced cVeg change by 52% (610%) of total cVeg change for MPI-ESM (CESM). The nonlocal effect of regions with largest carbon changes exhibit partly much larger nonlocal/total ratio. For instance, the nonlocal cVeg change in the Congo basin after cropland expansion accounts for more than 30% of total cVeg change. Furthermore, in some regions, the nonlocal effect of cVeg can be opposite to the local effect, and may thus reduce the total effect of the LMLCC practice compared to what would be expected from the local effect alone.Overall, the results from MPI-ESM and CESM indicate that the nonlocal carbon effect is important in key regions and can even become globally important for the irrigation practices. In addition to local effects, these unintended nonlocal effects need to be considered when the impacts of a LMLCC practice on the entire carbon cycle (e.g., also with regard to a potential carbon dioxide removal method) will be assessed.

Changes in the Greenland Blocking Index (GBI) present a threat to both the mass loss of the Greenland ice sheet, potentially leading to increased sea level rise, and downstream regions, such as the European continent and Mediterranean Basin that may be affected by associated changes in the frequency and characteristics of weather patterns. While a consistent increase in the summer GBI has been detected in (several) observational and reanalysis datasets, global climate models and seasonal prediction systems so far have failed in capturing this historical trend. The question whether the observed trend is real and not reproduced by models or an artifact of multidecadal variability (and, hence, a transient phenomenon) has so far not been answered. Here we apply the Peter and Clark momentary conditional independence (PCMCI) causal discovery tool to (i) unravel essential causal drivers of the GBI in a set of selected atmospheric fields at intraseasonal (weekly and 3-day averages) time scales in observations and (ii) compare the latter with those found in the ECMWF SEAS5 seasonal forecasts. Observed causal links well reproduced by the dynamical model would be considered indicative of the trend in GBI found in observations more likely emerging because of the selected time frame. By contrast, any differences between the two sets of causal drivers in observations and models would support the hypothesis that some key mechanisms are not appropriately represented in the forecast model, preventing it from correctly reproducing the observed trend.

During boreal summer, large scale subsidence and a persistent northerly flow, known as the Etesians, characterize the tropospheric circulation over the eastern Mediterranean, respectively bringing clear skies and mitigating the emergence of heat waves. Atmospheric drivers over both South Asia and the North Atlantic have been proposed to influence the intraseasonal variability of subsidence and Etesians over the eastern Mediterranean. Here, we employ Causal Effect Networks, obtained by applying the Peter and Clark Momentary Conditional Independence (PCMCI) causal discovery algorithm, to identify causal precursors of subsidence and Etesians in a set of atmospheric fields. We find that both wave train activity over the North Atlantic/North American region and convective activity over the northern Indian Ocean are causally related to the 3-day average 850 hPa meridional wind variations over the eastern Mediterranean at a lag of 3-to-6 days. For 3-day average 500 hPa vertical wind velocity, causal precursors over the Middle East and Arabian Sea similar to those identified for the Etesian are found. We further explore in detail the different nature of the causal precursors to the Etesians and subsidence over the eastern Mediterranean by applying varying averaging in the variables representing the involved phenomena.

Sub-seasonal forecasts are challenging for numerical weather prediction (NWP) and machine learning models alike. Predicting temperature with a lead-time of two or more weeks requires a forward model to integrate multiple complex interactions, like oceanic and land surface conditions that might lead to recurrent or persistent weather patterns. The representation of the relevant interactions is imperfect in NWP models, just as our physical understanding of them. Model predictability can therefore deviate from real predictability for poorly understood reasons, hindering future progress.This paper combines NWP with machine learning to detect and resolve such imperfect representations. We post-process ECMWF extended range forecasts of high summer temperatures in Europe with a shallow artificial neural network (ANN). Predictors are objectively selected from a large set of atmospheric, oceanic and terrestrial sources of predictability from ERA-5 and ECMWF re-forecast output. In the proposed architecture, the ANN learns to ‘update’ a prior ECMWF-given probability of two-meter temperature exceeding a given threshold. Due to the architecture of the network the magnitude of each correction, like increasing underestimated probabilities, can be attributed to specific predictors at initialization- or forecast-time. We interpret the circumstances in which substantial corrections are made. This reveals, e.g., that a tropical west Pacific sea surface temperature pattern is connected to high monthly average European temperature at a two-week lead-time. This teleconnection pattern is underestimated by the dynamical model and by correcting for this bias the ANN-based post-processing can thus improve forecast skill. We further find that the method does not readily increase skill when applied to other combinations of lead-time, averaging period and threshold, possibly due to non-stationarity in the data, lack of real predictability or lack of a re-forecast set of sufficient length.

Soy harvest failure events can severely impact farmers, insurance companies and raise global prices. Reliable seasonal forecasts of mis-harvests would allow stakeholders to prepare and take appropriate early action. However, especially for farmers, the reliability and lead-time of current prediction systems provide insufficient information to justify for within-season adaptation measures. Recent innovations increased our ability to generate reliable statistical seasonal forecasts. Here, we combine these innovations to predict the 1-3 poor soy harvest years in eastern US. We first use a clustering algorithm to spatially aggregate crop producing regions within the eastern US that are particularly sensitive to hot-dry weather conditions. Next, we use observational climate variables (sea surface temperature (SST) and soil moisture) to extract precursor timeseries at multiple lags. This allows the machine learning model to learn the low-frequency evolution, which carries important information for predictability. A selection based on causal inference allows for physically interpretable precursors. We show that the robust selected predictors are associated with the evolution of the horseshoe Pacific SST pattern, in line with previous research. We use the state of the horseshoe Pacific to identify years with enhanced predictability. We achieve very high forecast skill of poor harvests events, even 3 months prior to sowing, using a strict one-step-ahead train-test splitting. Over the last 25 years, 90% of the predicted events in February were correct. When operational, this forecast would enable farmers (and insurance/trading companies) to make informed decisions on adaption measures, e.g., selecting more drought-resistant cultivars, invest in insurance, change planting management.

Land cover and land management changes (LCLMC) have often been highlighted as crucial regarding climate change mitigation (e.g., enhanced carbon uptake on land through afforestation), but their potential for adaptation has also been suggested (e.g., local cooling through irrigation). Regarding the latter, the effects of LCLMC on the climate remain uncertain. LCLMC can have strong implications on surface moisture fluxes and have even been linked to changes in large scale atmospheric circulation. Here, we study the effects of three LCLMC (i) global afforestation, (ii) global cropland expansion and (iii) large-scale irrigation extension on climate by employing three fully coupled Earth System Models (CESM, MPI-ESM, and EC-EARTH). Sensitivity simulations were performed under present-day conditions and extreme LCLMC, of which the effects on moisture fluxes and atmospheric circulation are investigated. We do this by first analyzing the surface moisture fluxes using monthly precipitation and evaporation data to perform a moisture convergence analysis, before performing a moisture tracking analysis with the Water Accounting Model (WAM-2 layers) , this model solves the atmospheric moisture balance and requires sub-daily data from the sensitivity experiments as an input.Here we focus on the results from CESM, cropland expansion has shown to cause an average shift southward of the Intertropical convergence zone as well as a weakening in westerlies strength and consequent decrease in moisture transport. This causes an increase in continental moisture sources over most of the Northern Hemisphere. Afforestation, in contrast, shows an average shift northward of the Intertropical convergence zone and enhanced westerlies and moisture transport. Lastly, irrigation expansion enhances the moisture convergence over areas where irrigation is applied, causing an increase in both precipitation and evapotranspiration.

The mechanisms leading to the occurrence of extreme weather and climate events are varied and complex. They generally encompass a combination of dynamical and thermodynamical processes, as well as drivers external to the climate system, such as anthropogenic greenhouse gas emissions and land-use change. Here we present the ExtremeX multi-model intercomparison experiment, which was designed to investigate the contribution of dynamic and thermodynamic processes to recent weather and climate extremes. The numerical experiments are performed with three Earth System Models: CESM, MIROC, and EC-Earth. They include control experiments with interactive atmosphere and land surface conditions, and experiments where either the atmospheric circulation, soil moisture or both are constrained using observation-based values. The temporal evolution and magnitude of temperature anomalies during heatwaves is well represented in the experiments with constrained atmosphere. However, mean climatological biases in temperature and precipitation are not substantially reduced in any of the constrained experiments, highlighting the importance of error compensations and tuning in the standard model versions. The results further reveal that both atmospheric circulation patterns and soil moisture conditions substantially contribute to the occurrence of heat extremes. Soil moisture effects are particularly important in the tropics, the monsoon areas and the Great Plains of the United States.

The US agriculture system supplies more than one-third of globally-traded soybean and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and explore synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis, satellite-based evapotranspiration and root-zone soil moisture with sub-national crop yields using statistical methods that account for interaction effects. Our model can explain on average about half of the year-to-year yield variability (60 % on all years and 40 % on out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions, and less so during wet conditions. Compound and interacting hot and dry August conditions (defined by the 95th and 5th percentiles of temperature and soil moisture, respectively) reduce yields by 1.25 standard deviation. This sensitivity is, respectively, 6 and 3 times larger than the sensitivity to hot or dry conditions alone. Other important drivers of negative yield responses are lower evapotranspiration early in the season and lower minimum temperature late in the season, both likely reflecting an increased risk of frost. The sensitivity to the identified drivers varies across the spatial domain with higher latitudes, and thus colder regions, being less sensitive to hot-dry August months. Historic trends in identified drivers indicates that US soybean has generally benefited from recent shifts in weather. Overall warming conditions have reduced the risk of frost in early and late-season and potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over eastern US. Still, despite these positive changes, we show that the frequency of compound hot-dry August month has remained unchanged over 1946–2016. Moreover, in the longer term, climate models project substantially warmer summers for the continental US which likely creates risks for soybean production.

Pronounced circumglobal waves can trigger and maintain persistent summer weather conditions by remaining in their preferred phase-locked positions for several weeks in a row. This phenomenon, especially important for wave numbers 5 and 7, has been observed in recent years, but it is unclear whether climate models can reproduce circulation types and their surface imprints.Here we assess three climate models (EC-Earth3, CESM1.2, and MIROC5) for their representation of amplified circumglobal waves and associated surface imprints in summer (June, July and August) over 1979-2016. ERA5 reanalysis data is used as reference to assess the models’ performance. We run a series of modeling experiments to understand the source of biases in the climate models: free interactive atmosphere and soil moisture runs (AISI), atmospheric nudged runs (AFSI), soil moisture prescribed runs (AISF), and both atmosphere and soil moisture nudged experiments (AFSF).We show that all models reasonably well reproduce the climatological wave spectra. Further, both wave 5 and wave 7 are found to exhibit phase-locking behaviors across all models, resulting in similar wave patterns across the hemisphere as compared to reanalysis. The surface imprints are observed in the models as well, but depending on the model, the results vary in strength. We also found the biases in surface temperature and precipitation anomalies mainly come from the atmospheric circulation in the models as these biases reduced considerably from AISI runs to AFSI and AFSF runs where upper atmosphere levels were nudged. Nudging soil moisture also minimizes some biases in the models but not as obvious as nudging the atmosphere.

The US agriculture system supplies more than one third of globally traded soybean, of which 90% is produced under rainfed agriculture. This makes the commodity particularly sensitive to weather and climate variability. Previous research has shown that annually averaged climate conditions explain about a third of global crop yield variability. Additionally, although less studied so far, crops are sensitive to specific short-term weather conditions, isolated or co-occurring at key moments throughout the growing season. Here we aim to identify key within-season weather and climate variables that can explain soybean yield variability in the US while exploring synergies between drivers that can have compounding impacts. The study combines weather data from reanalysis and satellite-based evapotranspiration and root-zone soil moisture with sub-national crop yield estimates using statistical methods that account for interaction effects. We also analyze the historic changes in identified key driving conditions in order to explore the effects of current climatic trends on yields. Our preliminary results indicate positive yield response to higher minimum temperature early and late in the season whereas the largest effect on soybeans is driven by the harmful co-occurrence of high temperature and low moisture levels during the summer flowering period significantly reducing yields on average in the US by one standard deviation. The magnitude of the response to climate drivers varies across the spatial domain highlighting the need to focus on local and season specific management strategies. On the bright side, recent trends in temperature have not increased the likelihood of low yields. This is because the overall warming conditions reduce the risk of frost early and late in the season. Conversely, a peculiar cooling trend during the summer period attributed to agricultural land use is beneficial for yields when crops are most sensitive to high temperatures. Our study provides a detailed understanding of the current relationship between climate and soybean yields in the US. This is particularly relevant for adaptation and mitigation strategies aimed at avoiding low yields in a context of increasing food demand and climate change.

Persistent summer extremes, such as heatwaves and droughts, can have considerable impacts on nature and societies. There is evidence that weather persistence has increased in Europe over the past decades, in association to changes in atmosphere dynamics, but uncertainties remain and the driving forces are not yet well understood. Particularly for Europe, the jet stream may affect surface weather significantly by modulating the North Atlantic storm tracks. Here, we examine the hypothesis that high-latitude warming and decreased westerlies in summer result in more double jets, consisting of two distinct maxima of the zonal wind in the upper troposphere, over the Eurasian sector. Previous work has shown that such double jet states are related to persistent blocking-like circulation in the mid-latitudes. We adapt a dynamical perspective of heat extreme trends by looking at large scale circulation and in particular, changes in the zonal mean zonal wind in different levels of the upper troposphere. We define clusters of jet states with the use of Self-Organizing Maps and analyze their characteristics. We find an increase in frequency and persistence of a cluster of double jet states for the period 1979-2019 during July-August (in ERA5 reanalysis data). Those states are linked to increased surface temperature and more frequent heatwaves compared to climatology over western, central, and northern Europe. Significant positive double jet anomalies are found to be dominant in the days preceding and/or coinciding with some of the most intense historical heatwaves in Europe, such as those of 2003 and 2018. A linear regression analysis shows that the increase in frequency and persistence of double jet states may explain part of the strong upward trend in heat extremes over these European regions.

In the summer of 2020, extreme fires have raged in northeastern Siberia, many of them within the Arctic Circle burning in ecotonal larch forest and tundra ecosystems. This unprecedented increase in fire activity within the Arctic Circle has been linked to record-high temperatures measured in the region, as well as to high lightning activity.In mid-latitudes, the pronounced and long-lasting heatwaves of the last decade have been linked to amplified Rossby waves connected with weak atmospheric circulation. These amplified waves tend to phase-lock in preferred positions and thereby lead to more persistent summer weather. Linkages between atmospheric teleconnections and boreal wildfires exist for some regions, yet the influence of wave dynamics on arctic-boreal wildfires is unknown. We explored relationships between wave dynamics, heatwaves, and the unprecedented fire activity in Siberia in 2020 to assess whether the recent surge in arctic-boreal fires in Siberia is driven by large-scale atmospheric dynamics.We determined wave amplitudes as phase positions by applying fast Fourier transformation on weekly averaged mid- to high-latitudinal mean meridional wind velocities at the 250 mb level from ERA5 reanalysis data. Gridded percentage area burned between 2001 and 2020 was derived from the Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1). We then quantified the importance of Rossby wave patterns on fire activity clustered by latitude in eastern Siberia.

Land cover and land management (LCLM) changes have been highlighted for their critical role in low-end warming scenarios, both in terms of global mitigation and local adaptation. Yet the overall potential of LCLM options and their combination is still poorly understood. Here we model the climatic effects of four LCLM options using three state-of-the-art Earth system models, including the Community Earth System Model (CESM), the Max Planck Institute Earth System Model (MPI-ESM) and the European Consortium Earth System Model (EC-EARTH). The considered LCLM options represent idealized conditions:(i) a fully afforested world, (ii) a fully deforested world, (ii) a fully afforested world with extensive wood harvesting, and (iv) a fully deforested world with extensive irrigation. In these idealized sensitivity experiments, ran under present-day climate conditions, the effects of the different LCLM strategies represent an upper bound of the potential for global mitigation and local adaptation. To disentangle the local and non-local effects from the LCLM changes, a checkerboard perturbation, as proposed by Winckler et al. (2017) is applied.Our first results show that deforestation leads to a pronounced warming in 2m air temperature in CESM over most regions, being most pronounced in the tropics (up to 4°C). In contrast, in the boreal regions of North America and Asia, deforestation causes a ~1°C cooling in 2m air temperature. In CESM, the local effect seems to dominate the temperature response from deforestation, while the resulting non-local effect overall has a smaller magnitude. This contrasts to the effect from afforestation, of which the non-local component dominates the 2m air temperature signal. Afforestation indeed shows a strong local cooling in the tropics and a slight local warming in the temperate and boreal regions, yet, the local cooling is regionally offset by a global, non-local warming of up to 2 °C. In a next step, we will extend this analysis to the ensemble of Earth system models and increase our process-based understanding of these results and their implications on hot extremes as well as the effects on other temperature metrics (surface temperature and temperature of the lowest level of atmospheric column). Finally, we will perform a subgrid-scale comparison of the effects of LCLM on temperature.References:Winckler, J., Reick, C.H., Pongratz, J., 2017. Robust identification of local biogeophysical effects of land-cover change in a global climate model, American Meteorological society, 30(2), DOI: 10.1175/JCLI-D-16-0067.1

Heatwaves can have devastating impact on society and reliable early warnings at several weeks lead time are needed. Heatwaves are often associated with quasi-stationary Rossby waves, which interact with sea surface temperature (SST). Previous studies showed that north-Pacific SST can provide long-lead predictability for eastern U.S. temperature, moderated by an atmospheric Rossby wave. The exact mechanisms, however, are not well understood. Here we analyze Rossby waves associated with heatwaves in western and eastern US. Causal inference analyses reveal that both waves are characterized by positive ocean-atmosphere feedbacks at synoptic timescales, amplifying the waves. However, this positive feedback on short timescales is not the causal mechanism that leads to a long-lead SST signal. Only the eastern US shows a long-lead causal link from SSTs to the Rossby wave. We show that the long-lead SST signal derives from low-frequency PDO variability, providing the source of eastern US temperature predictability. We use this improved physical understanding to identify more reliable long-lead predictions. When, at the onset of summer, the Pacific is in a pronounced PDO phase, the SST signal is expected to persist throughout summer. These summers are characterized by a stronger ocean-boundary forcing, thereby more than doubling the eastern US temperature forecast skill, providing a temporary window of enhanced predictability.

Extreme summer weather often has devastating impacts on society when it lasts for many days. Stalling cyclones can lead to flooding and persistent hot-dry conditions can lead to health impacts and harvest losses. Global warming weakens the hemispheric-wide circulation in boreal summer, which has been shown in both observations and models using multiple circulation metrics. Until now, it is still largely unclear what this weakening implies for regional weather conditions, including their persistence. Using an advanced persistence metric, we show that summer weather has become more-persistent over 1979–2019. State-of-the-art climate models reproduce this upward trend in persistence indicating that it can be attributed to greenhouse gas forcing. Our persistence metric accounts for the full state of the atmosphere at any given moment and is strongly rooted in dynamical systems theory. Thereby it is able to detect dynamical changes previously unseen in more widely used clustering analyses that sharply reduce the amount of information used. We show that under future high-emission scenarios, summer weather will become increasingly more-persistent due to a weakening of the circulation. Most of this increase in persistence, and the associated societal risks, is avoided under an emission scenario compatible with the Paris agreement.

Land cover and land management (LCLM) changes are important sources of anthropogenic CO2 emissions, constituting about 10% of current annual CO2 emissions, or about one third of cumulative emissions over the industrial era. However, simulations with Earth system models (ESMs) show a large range of CO2 emissions from LCLM. Several reasons for the divergence in estimates have been identified, in particular differences in simulated biomass and soil carbon stocks, and if and which land management practices are included in models. The divergence of model estimates is particularly worrisome since LCLM practices are discussed as key mitigation tools or “negative emission technologies” to reach the temperature goals of Paris Agreement. In the LAMACLIMA project (land management for climate mitigation and adaptation) we therefore conduct a detailed analysis of several LCLM practices across three ESMs to improve our understanding about model uncertainties. The present study aims to quantify the effects of forest cover changes and wood harvesting on the global carbon cycle, globally important LCLM practices with relevance also for physical climate and economic production. We conduct idealized global experiments of deforestation, afforestation and wood harvesting over a 150-year simulation period under present climate. All forcings (solar, trace gases, aerosols) are held constant at present-day levels to isolate the climatic effects from different LCLM scenarios on the carbon cycle. All experiments are conducted by three different Earth system models (MPI-ESM, EC-EARTH and CESM) to quantify inter-model uncertainty and potentially uncover specific model biases. The analysis focuses on the transient response of carbon fluxes after the LCLM practice is in order to unravel model differences concerning temporal dynamics of LCLM effects and to show how quickly signals emerge that could potentially mitigate climate change. With this research, we will provide a deeper understanding about simulated LCLM effects on the carbon cycle and also report model uncertainties. Together with parallel efforts to quantify biogeophysical effects of LCLM, our study will also lead to assess the overall potential of LCLM as a means for land-based climate mitigation.

Land cover and land management (LCLM) changes have a high potential to influence the biogeophysical and biogeochemical earth system processes. The interaction of soil and vegetation with the atmosphere alternates the water, energy and momentum balance, in turn affecting the climate locally, as well as the climate of distant regions through teleconnection pathways. This, among others, might benefit or oppose risks to local and global breadbasket regions, impacting the crop yields. In this study, we conduct model experiments to assess the local and remote impact of LCLM changes, in particular global re-/afforestation and deforestation, with a focus on the large-scale boreal summer atmospheric circulation. We hypothesize that due to the dominant role of land-atmosphere feedbacks in this season, robust dynamical transformations take place due to the LCLM changes. The idealized model experiments consist of three fully coupled Earth System Models (EC-EARTH, MPI-ESM and CESM) that run under constant 2015 greenhouse forcing for 150 years. Globally the LCLM changes go through a sequence of unchanged grid boxes in a checkerboard approach as recent studies have done, in order to accurately separate the local from the non-local effects.

The impact of deforestation on climate is mostly pronounced through net carbon emissions (biogeochemical effects), leading to a global warming. However, deforestation also alters the water and energy cycles (biogeophysical effects), which can cause a local warming or cooling depending on the region. This can potentially offset or even exacerbate the initial global warming signal caused by the biogeochemical effect. The results of earth system models show a large spread on the magnitude of biogeophysical effects and can even vary on the sign of these impacts for some regions. Thus, uncovering the uncertainty related to the biogeophysical effect of deforestation is crucial, to better understand the potential of afforestation as a means for land-based climate mitigation. We investigate the biogeophysical effects of deforestation on climate by conducting idealised deforestation experiments consisting of a 150-year simulation. Greenhouse gas forcing is held constant at present-day levels to disentangle between the climatic effects from land use and from those due to anthropogenic climate change. The experiment is conducted by three different Earth System Models (MPI-ESM, EC-EARTH and CESM) to quantify inter-model uncertainty and potentially uncover specific model biases. A recently-developed checkerboard approach is applied to disentangle the local and non-local effect (i.e. remote impacts of deforestation due to changes in atmospheric dynamics) from deforestation (Winckler et al. 2019). This enables us to better determine the uncertainties across the models as well as to validate the local biogeophysical effects of deforestation using observational datasets. This is the first time that the checkerboard approach is applied on multi-model climate simulations and thus serves as a benchmark for the applicability of this approach. References: Winckler, J., Reick, C.H., Luyssaert, S., Cescatti, A., Stoy, P.C., Lejeune, Q., Raddatz, T., Chlond, A., Heidkamp, M., Pongratz, J., Different Response of surface temperature and air temperature to deforestation in climate models, Journal of Earth System Dynamics, doi: https://doi.org/10.5194/esd-2018-66

The succession of European surface weather patterns has limited predictability because disturbances quickly transfer to the large scale flow. Some aggregated statistic however, like the average temperature exceeding a threshold, can have extended predictability when adequate spatial scales, temporal scales and thresholds are chosen. This study benchmarks how the forecast skill horizon of probabilistic 2-meter temperature forecasts from the ECMWF sub-seasonal forecast system evolves with varying scales and thresholds. We apply temporal aggregation by rolling window averaging and spatial aggregation by hierarchical clustering. We verify 20 years of re-forecasts against the E-OBS data set and find that European predictability extends at maximum up to week 4. Simple aggregation and standard statistical post-processing extend the forecast skill horizon with two and three skillful days on average, respectively.The intuitive notion that higher levels of aggregation capture the larger scale and lower frequency variability and therefore tap into an extended predictability, holds in many cases. However, we show that the effect can saturate and that regional optimums exist, beyond which extra aggregation reduces the forecast skill horizon. We expect that such windows of predictability result from specific physical mechanisms that only modulate and extend predictability locally. To optimize sub-seasonal forecasts for Europe, aggregation should in certain cases thus be limited.

The Mediterranean (MED) basin is a hot-spot for climate change impacts. We present recently developed techniques derived from Dynamical System Theory to investigate long-term changes in compound hot-wet extremes over the MED. We use three reanalysis products, spanning a 40-year period from 1979 to 2018: ERA5, ERA-Interim and ERA5 10-member ensemble. From these datasets, we extract daily maximum temperature (degC) and total precipitation (mm), which we then use in the dynamical systems analysis.Results show that the strength of the dynamical coupling between hot and wet extremes increased significantly at both annual and summer (June-August) timescales during the reanalysis period. This means that, regardless of changes in the occurrence of individual hot or wet extremes, joint occurrences may be becoming more frequent.Compound hot-wet extremes mostly occur during July and August, and correspond to a low-pressure core over the Aegean Sea and the eastern MED. The increasing trends in compound extremes may be associated with surface MED warming. Such enhanced warming can therefore drive compound hot-wet extremes especially during the summer, when localised convection or mesoscale systems such as medicanes are responsible for extreme precipitation events.

The Arctic has warmed more than twice as fast as the global average since the late twentieth century, a phenomenon known as Arctic amplification (AA). Recently, there have been considerable advances in understanding the physical contributions to AA, and progress has been made in understanding the mechanisms that link it to midlatitude weather variability. Observational studies overwhelmingly support that AA is contributing to winter continental cooling. Although some model experiments support the observational evidence, most modelling results show little connection between AA and severe midlatitude weather or suggest the export of excess heating from the Arctic to lower latitudes. Divergent conclusions between model and observational studies, and even intramodel studies, continue to obfuscate a clear understanding of how AA is influencing midlatitude weather.

In a warmer world, the hydrological cycle will change in intensity and in its geographic behaviour. This, in turn, will change patterns of river flood and the risk associated with them. The Last In...

The summer of 2018 witnessed a number of extreme weather events such as heatwaves in North America, Western Europe and the Caspian Sea region, and rainfall extremes in South-East Europe and Japan that occurred near-simultaneously. Here we show that some of these extremes were connected by an amplified hemisphere-wide wavenumber 7 circulation pattern. We show that this pattern constitutes an important teleconnection in Northern Hemisphere summer associated with prolonged and above-normal temperatures in North America, Western Europe and the Caspian Sea region. This pattern was also observed during the European heatwaves of 2003, 2006 and 2015 among others. We show that the occurrence of this wave 7 pattern has increased over recent decades.

GOTHAM International Summer School on Global Teleconnections in the Earth’s Climate System – Processes, Modelling and Advanced Analysis Methods; Potsdam, Germany, 18–22 September 2017

This paper synthesizes what is known about the physical and biophysical impacts of climate change and their consequences for societies and development under different levels of global warming in Latin America and the Caribbean (LAC). Projections show increasing mean temperatures by up to 4.5 °C compared to pre-industrial by the end of this century across LAC. Associated physical impacts include altered precipitation regimes, a strong increase in heat extremes, higher risks of droughts and increasing aridity. Moreover, the mean intensity of tropical cyclones, as well as the frequency of the most intense storms, is projected to increase while sea levels are expected to rise by ~0.2–1.1 mm depending on warming level and region. Tropical glacier volume is found to decrease substantially, with almost complete deglaciation under high warming levels. The much larger glaciers in the southern Andes are less sensitive to warming and shrink on slower timescales. Runoff is projected to be reduced in Central America, the southern Amazon basin and southernmost South America, while river discharge may increase in the western Amazon basin and in the Andes in the wet season. However, in many regions, there is uncertainty in the direction of these changes as a result of uncertain precipitation projections and differences in hydrological models. Climate change will also reduce agricultural yields, livestock and fisheries, although there may be opportunities such as increasing rice yield in several LAC countries or higher fish catch potential in the southernmost South American waters. Species range shifts threaten terrestrial biodiversity, and there is a substantial risk of Amazon rainforest degradation with continuing warming. Coral reefs are at increasing risk of annual bleaching events from 2040 to 2050 onwards irrespective of the climate scenario. These physical and biophysical climate change impacts challenge human livelihoods through, e.g., decreasing income from fisheries, agriculture or tourism. Furthermore, there is evidence that human health, coastal infrastructures and energy systems are also negatively affected. This paper concludes that LAC will be severely affected by climate change, even under lower levels of warming, due to the potential for impacts to occur simultaneously and compound one another., This research has been funded through the World Bank Project “Turn Down the Heat: Confronting the New Climate Normal,” and we are grateful to everybody involved in this activity for making it a success.

Scientific Reports 7: Article number: 45242; published online: 27 March 2017; updated: 26 May 2017. This Article contains an error in Figure 1a, where the y-axis ‘Temperature (K)’ is incorrectly labelled as ‘Temperature (°C)’. The correct Figure 1 appears below.

This paper synthesizes what is known about the physical and biophysical impacts of climate change and their consequences for societies and development under different levels of global warming in Central Asia. Projections show mean temperatures increasing by up to 6.5 °C compared to pre-industrial by the end of this century across the region. Associated physical impacts include altered precipitation regimes, more frequent heat extremes and increasing aridity. Increasing rates of glacial and snow melt could lead to greater river runoff, but also to greater seasonality of runoff in the short term and to decreasing water availability in the medium term to long term. These changes have negative implications for the water availability in the region and for conflicting water demands between agriculture and hydropower. Climate change could mostly decrease crop yields, challenging food security, but in more northern regions there could also be positive effects. Studies on climate change impacts on energy systems are scarce and yield conflicting results, but the more regional study shows decreasing prospects for hydropower. The health of the population is already sensitive to heat extremes and is projected to be exposed to more frequent and prolonged heat waves in the future, among other potential health impacts. While the evidence for a link between climate and migration is weak, the rural-to-urban migration can be especially expected to intensify. The paper concludes that Central Asia will be severely affected by climate change even if the global mean temperature increase is limited to 2 °C above pre-industrial levels, due to the potential for impacts to occur simultaneously and compound one another as well as interactions with wider development challenges, while risks will be strongly amplified if this threshold is crossed., This research has been funded through the World Bank Project “Turn Down the Heat: Confronting the New Climate Normal,” and we are grateful to everybody involved in this activity for making it a success.