SEMIC: an efficient surface energy and mass balance model applied to the Greenland ice sheet

We present SEMIC, a Surface Energy and Mass balance model of Intermediate Complexity for snow-and ice-covered surfaces such as the Greenland ice sheet. SEMIC is fast enough for glacial cycle applications, making it a suitable replacement for simpler methods such as the positive degree day (PDD) method often used in ice sheet modelling. Our model explicitly calculates the main processes involved in the surface energy and mass balance, while maintaining a simple interface and requiring minimal data input to drive it. In this novel approach, we parameterise diurnal temperature variations in order to more realistically capture the daily thaw-freeze cycles that characterise the ice sheet mass balance. We show how to derive optimal model parameters for SEMIC specifically to reproduce surface characteristics and day-to-day variations similar to the regional climate model MAR (Modele Atmospherique Regional, version 2) and its incorporated multilayer snowpack model SISVAT (Soil Ice Snow Vegetation Atmosphere Transfer). A validation test shows that SEMIC simulates future changes in surface temperature and surface mass balance in good agreement with the more sophisticated multilayer snowpack model SISVAT included in MAR. With this paper, we present a physically based surface model to the ice sheet modelling community that is general enough to be used with in situ observations, climate model, or reanalysis data, and that is at the same time computationally fast enough for long-term integrations, such as glacial cycles or future climate change scenarios

Addendum: High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years.

Funder: European Research Council Grant 647797Abstract: The variability of climate has profoundly impacted a wide range of macroecological processes in the Late Quaternary. Our understanding of these has greatly benefited from palaeoclimate simulations, however, high-quality reconstructions of ecologically relevant climatic variables have thus far been limited to a few selected time periods. Here, we present a 0.5° resolution bias-corrected dataset of global monthly temperature, precipitation, cloud cover, relative humidity and wind speed, 17 bioclimatic variables, annual net primary productivity, leaf area index and biomes, covering the last 120,000 years at a temporal resolution of 1,000–2,000 years. We combined medium-resolution HadCM3 climate simulations of the last 120,000 years with high-resolution HadAM3H simulations of the last 21,000 years, and modern-era instrumental data. This allows for the temporal variability of small-scale features whilst ensuring consistency with observed climate. Our data make it possible to perform continuous-time analyses at a high spatial resolution for a wide range of climatic and ecological applications - such as habitat and species distribution modelling, dispersal and extinction processes, biogeography and bioanthropology

Climatic windows for human migration out of Africa in the past 300,000 years.

Whilst an African origin of modern humans is well established, the timings and routes of their expansions into Eurasia are the subject of heated debate, due to the scarcity of fossils and the lack of suitably old ancient DNA. Here, we use high-resolution palaeoclimate reconstructions to estimate how difficult it would have been for humans in terms of rainfall availability to leave the African continent in the past 300k years. We then combine these results with an anthropologically and ecologically motivated estimate of the minimum level of rainfall required by hunter-gatherers to survive, allowing us to reconstruct when, and along which geographic paths, expansions out of Africa would have been climatically feasible. The estimated timings and routes of potential contact with Eurasia are compatible with archaeological and genetic evidence of human expansions out of Africa, highlighting the key role of palaeoclimate variability for modern human dispersals

Different environmental variables predict body and brain size evolution in Homo.

Increasing body and brain size constitutes a key macro-evolutionary pattern in the hominin lineage, yet the mechanisms behind these changes remain debated. Hypothesized drivers include environmental, demographic, social, dietary, and technological factors. Here we test the influence of environmental factors on the evolution of body and brain size in the genus Homo over the last one million years using a large fossil dataset combined with global paleoclimatic reconstructions and formalized hypotheses tested in a quantitative statistical framework. We identify temperature as a major predictor of body size variation within Homo, in accordance with Bergmann's rule. In contrast, net primary productivity of environments and long-term variability in precipitation correlate with brain size but explain low amounts of the observed variation. These associations are likely due to an indirect environmental influence on cognitive abilities and extinction probabilities. Most environmental factors that we test do not correspond with body and brain size evolution, pointing towards complex scenarios which underlie the evolution of key biological characteristics in later Homo

A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years.

Curated global climate data have been generated from climate model outputs for the last 120,000 years, whereas reconstructions going back even further have been lacking due to the high computational cost of climate simulations. Here, we present a statistically-derived global terrestrial climate dataset for every 1,000 years of the last 800,000 years. It is based on a set of linear regressions between 72 existing HadCM3 climate simulations of the last 120,000 years and external forcings consisting of CO2, orbital parameters, and land type. The estimated climatologies were interpolated to 0.5° resolution and bias-corrected using present-day climate. The data compare well with the original HadCM3 simulations and with long-term proxy records. Our dataset includes monthly temperature, precipitation, cloud cover, and 17 bioclimatic variables. In addition, we derived net primary productivity and global biome distributions using the BIOME4 vegetation model. The data are a relevant source for different research areas, such as archaeology or ecology, to study the long-term effect of glacial-interglacial climate cycles for periods beyond the last 120,000 years

The impact of climate change on incomes and convergence in Africa

© 2019 Elsevier Ltd Climate change is projected to detrimentally affect African countries’ economic development, while income inequalities across economies is among the highest on the planet. However, it is projected that income levels would converge on the continent. Hitherto there is limited evidence on how climate change could affect projected income convergence, accelerating, slowing down, or even reversing this process. Here, we analyze convergence considering climate-change damages, by employing an economic model embedding the three dimensions of risks at the country-level: exposure, vulnerability and hazards. The results show (1) with historical mean climate-induced losses between 10 and 15 percent of GDP per capita growth, the majority of African economies are poorly adapted to their current climatic conditions, (2) Western and Eastern African countries are projected to be the most affected countries on the continent and (3) As a consequence of these heightened impacts on a number of countries, inequalities between countries are projected to widen in the high warming scenario compared to inequalities in the low and without warming scenarios. To mitigate the impacts of economic development and inequalities across countries, we stress (1) the importance of mitigation ambition and Africa's leadership in keeping global mean temperature increase below 1.5 °C, (2) the need to address the current adaptation deficit as soon as possible, (3) the necessity to integrate quantitatively climate risks in economic and development planning and finally (4) we advocate for the generalization of a special treatment for the most vulnerable countries to access climate-related finance. The analysis raises issues on the ability of African countries to reach their SDGs targets and the potential increasing risk of instability, migration across African countries, of decreased trade and economic cooperation opportunities as a consequence of climate change – exacerbating its negative consequences

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multidrug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDR-TB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e. the Moran’s coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird (spatial resolution = 0.61 m) data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centres, using a 10 m2 grid-based algorithm. Geographical information system (GIS)- gridded measurements of each health centre were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDRTB covariates. Pearson’s correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS® module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases, using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centres and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran’s coefficient into uncorrelated, orthogonal map pattern components which revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations

Hydrodynamical interaction of stellar and planetary winds: Effects of charge exchange and radiation pressure on the observed Ly α absorption

Lyman α observations of the transiting exoplanet HD 209458b enable the study of exoplanet exospheres exposed to stellar extreme ultraviolet (EUV) fluxes, as well as the interacting stellar wind properties. In this study we present 3D hydrodynamical models for the stellar-planetary wind interaction including radiation pressure and charge exchange, together with photoionization, recombination, and collisional ionization processes. Our models explore the contribution of the radiation pressure and charge exchange to the Ly α absorption profile in a hydrodynamical framework, and for a single set of stellar wind parameters appropriate for HD 209458. We find that most of the absorption is produced by the material from the planet, with a secondary contribution of neutralized stellar ions by charge exchange. At the same time, the hydrodynamic shock heats up the planetary material, resulting in a broad thermal profile. Meanwhile, the radiation pressure yields a small velocity shift of the absorbing material. While neither charge exchange nor radiation pressure provides enough neutrals at the velocity needed to explain the observations at -100 km s-1 individually, we find that the two effects combined with the broad thermal profile are able to explain the observations.Fil: Esquivel, A.. Universidad Nacional Autónoma de México. Instituto de Ciencias Nucleares; México. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Schneiter, Ernesto Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Stockholms Universitet; SueciaFil: Villarreal D'angelo, Carolina Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Sgró, Mario Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Krapp, Leonardo Javier. Stockholms Universitet; Suecia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentin

The PRIMAP-hist national historical emissions time series

To assess the history of greenhouse gas emissions and individual countries' contributions to emissions and climate change, detailed historical data are needed. We combine several published datasets to create a comprehensive set of emissions pathways for each country and Kyoto gas, covering the years 1850 to 2014 with yearly values, for all UNFCCC member states and most non-UNFCCC territories. The sectoral resolution is that of the main IPCC 1996 categories. Additional time series of CO2 are available for energy and industry subsectors. Country-resolved data are combined from different sources and supplemented using year-to-year growth rates from regionally resolved sources and numerical extrapolations to complete the dataset. Regional deforestation emissions are downscaled to country level using estimates of the deforested area obtained from potential vegetation and simulations of agricultural land. In this paper, we discuss the data sources and methods used and present the resulting dataset, including its limitations and uncertainties. The dataset is available from doi:10.5880/PIK.2016.003 and can be viewed on the website accompanying this paper (http://www.pik-potsdam.de/primap-live/primap-hist/)

Range and extinction dynamics of the steppe bison in Siberia : A pattern-oriented modelling approach

Aim To determine the ecological processes and drivers of range collapse, population decline and eventual extinction of the steppe bison in Eurasia. Location Siberia. Time period Pleistocene and Holocene. Major taxa studied Steppe bison (Bison priscus). Methods We configured 110,000 spatially explicit population models (SEPMs) of climate-human-steppe bison interactions in Siberia, which we ran at generational time steps from 50,000 years before present. We used pattern-oriented modelling (POM) and fossil-based inferences of distribution and demographic change of steppe bison to identify which SEPMs adequately simulated important interactions between ecological processes and biological threats. These "best models" were then used to disentangle the mechanisms that were integral in the population decline and later extinction of the steppe bison in its last stronghold in Eurasia. Results Our continuous reconstructions of the range and extinction dynamics of steppe bison were able to reconcile inferences of spatio-temporal occurrence and the timing and location of extinction in Siberia based on hundreds of radiocarbon-dated steppe bison fossils. We showed that simulating the ecological pathway to extinction for steppe bison in Siberia in the early Holocene required very specific ecological niche constraints, demographic processes and a constrained synergy of climate and human hunting dynamics during the Pleistocene-Holocene transition. Main conclusions Ecological processes and drivers that caused ancient population declines of species can be reconstructed at high spatio-temporal resolutions using SEPMs and POM. Using this approach, we found that climatic change and hunting by humans are likely to have interacted with key ecological processes to cause the extinction of the steppe bison in its last refuge in Eurasia.Peer reviewe