M. Liberato, I. Montero, C. Gouveia, A. Russo, A. Ramos, R. Trigo
{"title":"Rankings of extreme and widespread dry and wet events in the Iberian Peninsula between 1901 and 2016","authors":"M. Liberato, I. Montero, C. Gouveia, A. Russo, A. Ramos, R. Trigo","doi":"10.5194/ESD-12-197-2021","DOIUrl":"https://doi.org/10.5194/ESD-12-197-2021","url":null,"abstract":"Abstract. Extensive, long-standing dry and wet episodes are two of\u0000the most frequent climatic extreme events in the Iberian Peninsula. Here, a\u0000method for ranking regional extremes of persistent, widespread drought and\u0000wet events is presented, considering different timescales. The method is\u0000based on the multi-scalar Standardized Precipitation Evapotranspiration Index\u0000(SPEI) gridded dataset for the Iberian Peninsula. Climatic Research Unit\u0000(CRU) data are used to compute the SPEI between 1901 and 2016 by means of a\u0000log-logistic probability distribution function. The potential\u0000evapotranspiration (PET) is computed using the Penman–Monteith equation.\u0000The ranking classification method is based on the assessment of the\u0000magnitude of an event, which is obtained after considering both the area affected by the respective dryness or wetness – defined by SPEI values over a\u0000certain threshold – and its intensity in each grid point. A sensitivity\u0000analysis of the impact of different thresholds used to define dry and wet events\u0000is also performed. For both the dry and wet periods, this simple yet robust tool\u0000allows for the identification and ranking of well-known regional extremes of persistent,\u0000extensive dry and wet periods at different timescales. A comprehensive\u0000dataset of rankings of the most extreme, prolonged, widespread dry and wet\u0000periods in the Iberian Peninsula is presented for aggregated timescales of\u00006, 12, 18, and 24 months. Results show that no region in the Iberian Peninsula is more prone to the occurrence of any of these long-term (dry\u0000and/or wet) extreme events. Finally, it is highlighted that the\u0000application of this methodology to other domains and periods represents an\u0000important tool for extensive, long-standing, extreme event assessment\u0000worldwide.","PeriodicalId":11466,"journal":{"name":"Earth System Dynamics Discussions","volume":"12 1","pages":"197-210"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84911525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Spring, I. Dunkl, Hongmei Li, V. Brovkin, T. Ilyina
{"title":"Trivial improvements of predictive skill due to direct reconstruction\u0000of global carbon cycle","authors":"A. Spring, I. Dunkl, Hongmei Li, V. Brovkin, T. Ilyina","doi":"10.5194/ESD-2021-4","DOIUrl":"https://doi.org/10.5194/ESD-2021-4","url":null,"abstract":"Abstract. State-of-the-art carbon cycle prediction systems are initialized from reconstruction simulations in which state variables of the climate system are assimilated. While currently only the physical state variables are assimilated, biogeochemical state variables adjust to the state acquired through this assimilation indirectly instead of being assimilated themselves. In the absence of comprehensive biogeochemical reanalysis products, such approach is pragmatic. Here we evaluate a potential advantage of having perfect carbon cycle observational products to be used for direct carbon cycle reconstruction. Within an idealized perfect-model framework, we define 50 years of a control simulation under pre-industrial CO2 levels as our target representing observations. We nudge variables from this target onto arbitrary initial conditions 150 years later mimicking an assimilation simulation generating initial conditions for hindcast experiments of prediction systems. We investigate the tracking performance, i.e. bias, correlation and root-mean-square-error between the reconstruction and the target, when nudging an increasing set of atmospheric, oceanic and terrestrial variables with a focus on the global carbon cycle explaining variations in atmospheric CO2. We compare indirect versus direct carbon cycle reconstruction against a resampled threshold representing internal variability. Afterwards, we use these reconstructions to initialize ensembles to assess how well the target can be predicted after reconstruction. Interested in the ability to reconstruct global atmospheric CO2, we focus on the global carbon cycle reconstruction and predictive skill. We find that indirect carbon cycle reconstruction through physical fields reproduces the target variations on a global and regional scale much better than the resampled threshold. While reproducing the large scale variations, nudging introduces systematic regional biases in the physical state variables, on which biogeochemical cycles react very sensitively. Global annual surface oceanic pCO2 initial conditions are indirectly reconstructed with an anomaly correlation coefficient (ACC) of 0.8 and debiased root mean square error (RMSE) of 0.3 ppm. Direct reconstruction slightly improves initial conditions in ACC by +0.1 and debiased RMSE by −0.1 ppm. Indirect reconstruction of global terrestrial carbon cycle initial conditions for vegetation carbon pools track the target by ACC of 0.5 and debiased RMSE of 0.5 PgC. Direct reconstruction brings negligible improvements for air-land CO2 flux. Global atmospheric CO2 is indirectly tracked by ACC of 0.8 and debiased RMSE of 0.4 ppm. Direct reconstruction of the marine and terrestrial carbon cycles improves ACC by 0.1 and debiased RMSE by −0.1 ppm. We find improvements in global carbon cycle predictive skill from direct reconstruction compared to indirect reconstruction. After correcting for mean bias, indirect and direct reconstruction both predict the target simil","PeriodicalId":11466,"journal":{"name":"Earth System Dynamics Discussions","volume":"110 1","pages":"1-36"},"PeriodicalIF":0.0,"publicationDate":"2021-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79545538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}