2020, Straaten, Chiem, Whan, Kirien, Coumou, Dim, Hurk, Bart, Schmeits, Maurice

The succession of European surface weather patterns has limited predictability because disturbances quickly transfer to the large-scale flow. Some aggregated statistics, however, such as 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-m temperature forecasts from the subseasonal forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) 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 dataset and find that European predictability extends at maximum into the fourth week. Simple aggregation and standard statistical post-processing extend the forecast skill horizon with two and three skilful days on average, respectively. The intuitive notion that higher levels of aggregation capture large-scale and low-frequency variability and can therefore tap into extended predictability holds in many cases. However, we show that the effect can be saturated and that there exist regional optimums beyond which extra aggregation reduces the forecast skill horizon. We expect such windows of predictability to result from specific physical mechanisms that only modulate and extend predictability locally. To optimize subseasonal forecasts for Europe, aggregation should thus be limited in certain cases.

2021, Fernandez-Palomino, Carlos Antonio, Hattermann, Fred F., Krysanova, Valentina, Vega-Jácome, Fiorella, Bronstert, Axel

Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve–FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography. © 2020 IAHS.

2020, Seroussi, Hélène, Nowicki, Sophie, Payne, Antony J., Goelzer, Heiko, Lipscomb, William H., Abe-Ouchi, Ayako, Agosta, Cécile, Albrecht, Torsten, Asay-Davis, Xylar, Barthel, Alice, Calov, Reinhard, Cullather, Richard, Dumas, Christophe, Galton-Fenzi, Benjamin K., Gladstone, Rupert, Golledge, Nicholas R., Gregory, Jonathan M., Greve, Ralf, Hattermann, Tore, Hoffman, Matthew J., Humbert, Angelika, Huybrechts, Philippe, Jourdain, Nicolas C., Kleiner, Thomas, Larour, Eric, Leguy, Gunter R., Lowry, Daniel P., Little, Chistopher M., Morlighem, Mathieu, Pattyn, Frank, Pelle, Tyler, Price, Stephen F., Quiquet, Aurélien, Reese, Ronja, Schlegel, Nicole-Jeanne, Shepherd, Andrew, Simon, Erika, Smith, Robin S., Straneo, Fiammetta, Sun, Sainan, Trusel, Luke D., Van Breedam, Jonas, van de Wal, Roderik S. W., Winkelmann, Ricarda, Zhao, Chen, Zhang, Tong, Zwinger, Thomas

Ice flow models of the Antarctic ice sheet are commonly used to simulate its future evolution in response to different climate scenarios and assess the mass loss that would contribute to future sea level rise. However, there is currently no consensus on estimates of the future mass balance of the ice sheet, primarily because of differences in the representation of physical processes, forcings employed and initial states of ice sheet models. This study presents results from ice flow model simulations from 13 international groups focusing on the evolution of the Antarctic ice sheet during the period 2015-2100 as part of the Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). They are forced with outputs from a subset of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), representative of the spread in climate model results. Simulations of the Antarctic ice sheet contribution to sea level rise in response to increased warming during this period varies between 7:8 and 30.0 cm of sea level equivalent (SLE) under Representative Concentration Pathway (RCP) 8.5 scenario forcing. These numbers are relative to a control experiment with constant climate conditions and should therefore be added to the mass loss contribution under climate conditions similar to presentday conditions over the same period. The simulated evolution of the West Antarctic ice sheet varies widely among models, with an overall mass loss, up to 18.0 cm SLE, in response to changes in oceanic conditions. East Antarctica mass change varies between 6:1 and 8.3 cm SLE in the simulations, with a significant increase in surface mass balance outweighing the increased ice discharge under most RCP 8.5 scenario forcings. The inclusion of ice shelf collapse, here assumed to be caused by large amounts of liquid water ponding at the surface of ice shelves, yields an additional simulated mass loss of 28mm compared to simulations without ice shelf collapse. The largest sources of uncertainty come from the climate forcing, the ocean-induced melt rates, the calibration of these melt rates based on oceanic conditions taken outside of ice shelf cavities and the ice sheet dynamic response to these oceanic changes. Results under RCP 2.6 scenario based on two CMIP5 climate models show an additional mass loss of 0 and 3 cm of SLE on average compared to simulations done under present-day conditions for the two CMIP5 forcings used and display limited mass gain in East Antarctica. © Author(s) 2020.

2021, Hoffmann, Roman, Šedová, Barbora, Vinke, Kira

The question whether and how climatic factors influence human migration has gained both academic and public interest in the past years. Based on two meta-analyses, this paper systematically reviews the quantitative empirical literature on climate-related migration from a methodological perspective. In total, information from 127 original micro- and macro-level studies is analyzed to assess how different concepts, research designs, and analytical methods shape our understanding of climate migration. We provide an overview of common methodological approaches and present evidence on their potential implications for the estimation of climatic impacts. We identify five key challenges, which relate to the i) measurement of migration and ii) climatic events, iii) the integration and aggregation of data, iv) the identification of causal relationships, and v) the exploration of contextual influences and mechanisms. Advances in research and modelling are discussed together with best practice cases to provide guidance to researchers studying the climate-migration nexus. We recommend for future empirical studies to employ approaches that are of relevance for and reflect local contexts, ensuring high levels of comparability and transparency.

2021, Lu, Zhenghui, Yuan, Naiming, Yang, Qing, Ma, Zhuguo, Kurths, Jürgen

Obtaining an efficient prediction of the Pacific Decadal Oscillation (PDO) phase transition is a worldwide challenge. Here, we employed the climate network analysis to uncover early warning signals prior to a PDO phase transition. This way an examination of cooperative behavior in the PDO region revealed an enhanced signal that propagated from the western Pacific to the northwest coast of North America. The detection of this signal corresponds very well to the time when the upper ocean heat content in the off-equatorial northwestern tropical Pacific reaches a threshold, in which case a PDO phase transition may be expected with the arising of the next El Ni urn:x-wiley:00948276:media:grl61986:grl61986-math-0001o/La Niurn:x-wiley:00948276:media:grl61986:grl61986-math-0002 a event. The objectively detected early warning signal successfully forewarned all the six PDO phase transitions from the 1890–2000, and also underpinned the possible PDO phase transition around 2015, which may be triggered by the strong El Niurn:x-wiley:00948276:media:grl61986:grl61986-math-0003o event in 2015–2016.

2020, Lehmann, J., Kretschmer, M., Schauberger, B., Wechsung, F.

Wheat production plays an important role in Morocco. Current wheat forecast systems use weather and vegetation data during the crop growing phase, thus limiting the earliest possible release date to early spring. However, Morocco's wheat production is mostly rainfed and thus strongly tied to fluctuations in rainfall, which in turn depend on slowly evolving climate dynamics. This offers a source of predictability at longer time scales. Using physically guided causal discovery algorithms, we extract climate precursors for wheat yield variability from gridded fields of geopotential height and sea surface temperatures which show potential for accurate yield forecasts already in December, with around 50% explained variance in an out-of-sample cross validation. The detected interactions are physically meaningful and consistent with documented ocean-atmosphere feedbacks. Reliable yield forecasts at such long lead times could provide farmers and policy makers with necessary information for early action and strategic adaptation measurements to support food security. ©2020. The Authors.

2021, Wang, Lei, Song, Chunqiao, Conradt, Tobias, Rasmy, Mohamed, Li, Xiuping

[No abstract available]

2020, Cornford, Stephen L., Seroussi, Helene, Asay-Davis, Xylar S., Gudmundsson, G. Hilmar, Arthern, Rob, Borstad, Chris, Christmann, Julia, dos Santos, Thiago Dias, Feldmann, Johannes, Goldberg, Daniel, Hoffman, Matthew J., Humbert, Angelika, Kleiner, Thomas, Leguy, Gunter, Lipscomb, William H., Merino, Nacho, Durand, Gaël, Morlighem, Mathieu, Pollard, David, Rückamp, Martin, Williams, C. Rosie, Yu, Hongju

We present the result of the third Marine Ice Sheet Model Intercomparison Project, MISMIP+. MISMIP+ is intended to be a benchmark for ice-flow models which include fast sliding marine ice streams and floating ice shelves and in particular a treatment of viscous stress that is sufficient to model buttressing, where upstream ice flow is restrained by a downstream ice shelf. A set of idealized experiments first tests that models are able to maintain a steady state with the grounding line located on a retrograde slope due to buttressing and then explore scenarios where a reduction in that buttressing causes ice stream acceleration, thinning, and grounding line retreat. The majority of participating models passed the first test and then produced similar responses to the loss of buttressing. We find that the most important distinction between models in this particular type of simulation is in the treatment of sliding at the bed, with other distinctions - notably the difference between the simpler and more complete treatments of englacial stress but also the differences between numerical methods - taking a secondary role. © 2020 Wolters Kluwer Medknow Publications. All rights reserved.

2020, Kuempel, Caitlin D., Frazier, Melanie, Nash, Kirsty L., Jacobsen, Nis Sand, Williams, David R., Blanchard, Julia L., Cottrell, Richard S., McIntyre, Peter B., Moran, Daniel, Bouwman, Lex, Froehlich, Halley E., Gephart, Jessica A., Metian, Marc, Többen, Johannes, Halpern, Benjamin S.

Producing food exerts pressures on the environment. Understanding the location and magnitude of food production is key to reducing the impacts of these pressures on nature and people. In this Perspective, Kuempel et al. outline an approach for integrating life cycle assessment and cumulative impact mapping data and methodologies to map the cumulative environmental pressure of food systems. The approach enables quantification of current and potential future environmental pressures, which are needed to reduce the net impact of feeding humanity. © 2020 The AuthorsFeeding a growing, increasingly affluent population while limiting environmental pressures of food production is a central challenge for society. Understanding the location and magnitude of food production is key to addressing this challenge because pressures vary substantially across food production types. Applying data and models from life cycle assessment with the methodologies for mapping cumulative environmental impacts of human activities (hereafter cumulative impact mapping) provides a powerful approach to spatially map the cumulative environmental pressure of food production in a way that is consistent and comprehensive across food types. However, these methodologies have yet to be combined. By synthesizing life cycle assessment and cumulative impact mapping methodologies, we provide guidance for comprehensively and cumulatively mapping the environmental pressures (e.g., greenhouse gas emissions, spatial occupancy, and freshwater use) associated with food production systems. This spatial approach enables quantification of current and potential future environmental pressures, which is needed for decision makers to create more sustainable food policies and practices. © 2020 The Authors

2020, Fuchs, Kathrin, Merbold, Lutz, Buchmann, Nina, Bretscher, Daniel, Brilli, Lorenzo, Fitton, Nuala, Topp, Cairistiona F.E., Klumpp, Katja, Lieffering, Mark, Martin, Raphaël, Newton, Paul C.D., Rees, Robert M., Rolinski, Susanne, Smith, Pete, Snow, Val

Process-based models are useful for assessing the impact of changing management practices and climate on yields and greenhouse gas (GHG) emissions from agricultural systems such as grasslands. They can be used to construct national GHG inventories using a Tier 3 approach. However, accurate simulations of nitrous oxide (N2O) fluxes remain challenging. Models are limited by our understanding of soil-plant-microbe interactions and the impact of uncertainty in measured input parameters on simulated outputs. To improve model performance, thorough evaluations against in situ measurements are needed. Experimental data of N2O emissions under two management practices (control with typical fertilization versus increased clover and no fertilization) were acquired in a Swiss field experiment. We conducted a multimodel evaluation with three commonly used biogeochemical models (DayCent in two variants, PaSim, APSIM in two variants) comparing four years of data. DayCent was the most accurate model for simulating N2O fluxes on annual timescales, while APSIM was most accurate for daily N2O fluxes. The multimodel ensemble average reduced the error in estimated annual fluxes by 41% compared to an estimate using the Intergovernmental Panel on Climate Change (IPCC)-derived method for the Swiss agricultural GHG inventory (IPCC-Swiss), but individual models were not systematically more accurate than IPCC-Swiss. The model ensemble overestimated the N2O mitigation effect of the clover-based treatment (measured: 39–45%; ensemble: 52–57%) but was more accurate than IPCC-Swiss (IPCC-Swiss: 72–81%). These results suggest that multimodel ensembles are valuable for estimating the impact of climate and management on N2O emissions. ©2019. The Authors.