Search Results
Trend detection in river flow indices in Poland
The issue of trend detection in long time series of river flow records is of vast theoretical interest and considerable practical relevance. Water management is based on the assumption of stationarity; hence, it is crucial to check whether taking this assumption is justified. The objective of this study is to analyse long-term trends in selected river flow indices in small- and medium-sized catchments with relatively unmodified flow regime (semi-natural catchments) in Poland. The examined indices describe annual and seasonal average conditions as well as annual extreme conditions—low and high flows. The special focus is on the spatial analysis of trends, carried out on a comprehensive, representative data set of flow gauges. The present paper is timely, as no spatially comprehensive studies (i.e. covering the entire Poland or its large parts) on trend detection in time series of river flow have been done in the recent 15 years or so. The results suggest that there is a strong random component in the river flow process, the changes are weak and the spatial pattern is complex. Yet, the results of trend detection in different indices of river flow in Poland show that there exists a spatial divide that seems to hold quite generally for various indices (annual, seasonal, as well as low and high flow). Decreases of river flow dominate in the northern part of the country and increases usually in the southern part. Stations in the central part show mostly ‘no trend’ results. However, the spatial gradient is apparent only for the data for the period 1981–2016 rather than for 1956–2016. It seems also that the magnitude of increases of river flow is generally lower than that of decreases.
Analytically tractable climate–carbon cycle feedbacks under 21st century anthropogenic forcing
Changes to climate–carbon cycle feedbacks may significantly affect the Earth system's response to greenhouse gas emissions. These feedbacks are usually analysed from numerical output of complex and arguably opaque Earth system models. Here, we construct a stylised global climate–carbon cycle model, test its output against comprehensive Earth system models, and investigate the strengths of its climate–carbon cycle feedbacks analytically. The analytical expressions we obtain aid understanding of carbon cycle feedbacks and the operation of the carbon cycle. Specific results include that different feedback formalisms measure fundamentally the same climate–carbon cycle processes; temperature dependence of the solubility pump, biological pump, and CO2 solubility all contribute approximately equally to the ocean climate–carbon feedback; and concentration–carbon feedbacks may be more sensitive to future climate change than climate–carbon feedbacks. Simple models such as that developed here also provide "workbenches" for simple but mechanistically based explorations of Earth system processes, such as interactions and feedbacks between the planetary boundaries, that are currently too uncertain to be included in comprehensive Earth system models.
The importance of snow albedo for ice sheet evolution over the last glacial cycle
The surface energy and mass balance of ice sheets strongly depends on the amount of solar radiation absorbed at the surface, which is mainly controlled by the albedo of snow and ice. Here, using an Earth system model of intermediate complexity, we explore the role played by surface albedo for the simulation of glacial cycles. We show that the evolution of the Northern Hemisphere ice sheets over the last glacial cycle is very sensitive to the representation of snow albedo in the model. It is well known that the albedo of snow depends strongly on snow grain size and the content of light-absorbing impurities. Excluding either the snow aging effect or the dust darkening effect on snow albedo leads to an excessive ice build-up during glacial times and consequently to a failure in simulating deglaciation. While the effect of snow grain growth on snow albedo is well constrained, the albedo reduction due to the presence of dust in snow is much more uncertain because the light-absorbing properties of dust vary widely as a function of dust mineral composition. We also show that assuming slightly different optical properties of dust leads to very different ice sheet and climate evolutions in the model. Conversely, ice sheet evolution is less sensitive to the choice of ice albedo in the model. We conclude that a proper representation of snow albedo is a fundamental prerequisite for a successful simulation of glacial cycles.
The effect of overshooting 1.5 °C global warming on the mass loss of the Greenland ice sheet
Sea-level rise associated with changing climate is expected to pose a major challenge for societies. Based on the efforts of COP21 to limit global warming to 2.0 ∘C or even 1.5 ∘C by the end of the 21st century (Paris Agreement), we simulate the future contribution of the Greenland ice sheet (GrIS) to sea-level change under the low emission Representative Concentration Pathway (RCP) 2.6 scenario. The Ice Sheet System Model (ISSM) with higher-order approximation is used and initialized with a hybrid approach of spin-up and data assimilation. For three general circulation models (GCMs: HadGEM2-ES, IPSL-CM5A-LR, MIROC5) the projections are conducted up to 2300 with forcing fields for surface mass balance (SMB) and ice surface temperature (Ts) computed by the surface energy balance model of intermediate complexity (SEMIC). The projected sea-level rise ranges between 21–38 mm by 2100 and 36–85 mm by 2300. According to the three GCMs used, global warming will exceed 1.5 ∘C early in the 21st century. The RCP2.6 peak and decline scenario is therefore manually adjusted in another set of experiments to suppress the 1.5 ∘C overshooting effect. These scenarios show a sea-level contribution that is on average about 38 % and 31 % less by 2100 and 2300, respectively. For some experiments, the rate of mass loss in the 23rd century does not exclude a stable ice sheet in the future. This is due to a spatially integrated SMB that remains positive and reaches values similar to the present day in the latter half of the simulation period. Although the mean SMB is reduced in the warmer climate, a future steady-state ice sheet with lower surface elevation and hence volume might be possible. Our results indicate that uncertainties in the projections stem from the underlying GCM climate data used to calculate the surface mass balance. However, the RCP2.6 scenario will lead to significant changes in the GrIS, including elevation changes of up to 100 m. The sea-level contribution estimated in this study may serve as a lower bound for the RCP2.6 scenario, as the currently observed sea-level rise is not reached in any of the experiments; this is attributed to processes (e.g. ocean forcing) not yet represented by the model, but proven to play a major role in GrIS mass loss.
Assessment of climate change and associated impact on selected sectors in Poland
The present paper offers a brief assessment of climate change and associated impact in Poland, based on selected results of the Polish–Norwegian CHASE-PL project. Impacts are examined in selected sectors, such as water resources, natural hazard risk reduction, environment, agriculture and health. Results of change detection in long time series of observed climate and climate impact variables in Poland are presented. Also, projections of climate variability and change are provided for time horizons of 2021–2050 and 2071–2100 for two emission scenarios, RCP4.5 and RCP8.5 in comparison with control period, 1971–2000. Based on climate projections, examination of future impacts on sectors is also carried out. Selected uncertainty issues relevant to observations, understanding and projections are tackled as well.
Changes in crop yields and their variability at different levels of global warming
An assessment of climate change impacts at different levels of global warming is crucial to inform the policy discussion about mitigation targets, as well as for the economic evaluation of climate change impacts. Integrated assessment models often use global mean temperature change (ΔGMT) as a sole measure of climate change and, therefore, need to describe impacts as a function of ΔGMT. There is already a well-established framework for the scalability of regional temperature and precipitation changes with ΔGMT. It is less clear to what extent more complex biological or physiological impacts such as crop yield changes can also be described in terms of ΔGMT, even though such impacts may often be more directly relevant for human livelihoods than changes in the physical climate. Here we show that crop yield projections can indeed be described in terms of ΔGMT to a large extent, allowing for a fast estimation of crop yield changes for emissions scenarios not originally covered by climate and crop model projections. We use an ensemble of global gridded crop model simulations for the four major staple crops to show that the scenario dependence is a minor component of the overall variance of projected yield changes at different levels of ΔGMT. In contrast, the variance is dominated by the spread across crop models. Varying CO2 concentrations are shown to explain only a minor component of crop yield variability at different levels of global warming. In addition, we find that the variability in crop yields is expected to increase with increasing warming in many world regions. We provide, for each crop model, geographical patterns of mean yield changes that allow for a simplified description of yield changes under arbitrary pathways of global mean temperature and CO2 changes, without the need for additional climate and crop model simulations.
The impact of uncertainty on optimal emission policies
We apply a computational framework for specifying and solving sequential decision problems to study the impact of three kinds of uncertainties on optimal emission policies in a stylized sequential emission problem.We find that uncertainties about the implementability of decisions on emission reductions (or increases) have a greater impact on optimal policies than uncertainties about the availability of effective emission reduction technologies and uncertainties about the implications of trespassing critical cumulated emission thresholds. The results show that uncertainties about the implementability of decisions on emission reductions (or increases) call for more precautionary policies. In other words, delaying emission reductions to the point in time when effective technologies will become available is suboptimal when these uncertainties are accounted for rigorously. By contrast, uncertainties about the implications of exceeding critical cumulated emission thresholds tend to make early emission reductions less rewarding.
On deeper human dimensions in Earth system analysis and modelling
While humanity is altering planet Earth at unprecedented magnitude and speed, representation of the cultural driving factors and their dynamics in models of the Earth system is limited. In this review and perspectives paper, we argue that more or less distinct environmental value sets can be assigned to religion – a deeply embedded feature of human cultures, here defined as collectively shared belief in something sacred. This assertion renders religious theories, practices and actors suitable for studying cultural facets of anthropogenic Earth system change, especially regarding deeper, non-materialistic motivations that ask about humans' self-understanding in the Anthropocene epoch. We sketch a modelling landscape and outline some research primers, encompassing the following elements: (i) extensions of existing Earth system models by quantitative relationships between religious practices and biophysical processes, building on databases that allow for (mathematical) formalisation of such knowledge; (ii) design of new model types that specifically represent religious morals, actors and activities as part of co-evolutionary human–environment dynamics; and (iii) identification of research questions of humanitarian relevance that are underrepresented in purely economic–technocratic modelling and scenario paradigms. While this analysis is by necessity heuristic and semi-cohesive, we hope that it will act as a stimulus for further interdisciplinary and systematic research on the immaterial dimension of humanity's imprint on the Earth system, both qualitatively and quantitatively.
Temperature-related mortality impacts under and beyond Paris Agreement climate change scenarios
The Paris Agreement binds all nations to undertake ambitious efforts to combat climate change, with the commitment to “hold warming well below 2 °C in global mean temperature (GMT), relative to pre-industrial levels, and to pursue efforts to limit warming to 1.5 °C”. The 1.5 °C limit constitutes an ambitious goal for which greater evidence on its benefits for health would help guide policy and potentially increase the motivation for action. Here we contribute to this gap with an assessment on the potential health benefits, in terms of reductions in temperature-related mortality, derived from the compliance to the agreed temperature targets, compared to more extreme warming scenarios. We performed a multi-region analysis in 451 locations in 23 countries with different climate zones, and evaluated changes in heat and cold-related mortality under scenarios consistent with the Paris Agreement targets (1.5 and 2 °C) and more extreme GMT increases (3 and 4 °C), and under the assumption of no changes in demographic distribution and vulnerability. Our results suggest that limiting warming below 2 °C could prevent large increases in temperature-related mortality in most regions worldwide. The comparison between 1.5 and 2 °C is more complex and characterized by higher uncertainty, with geographical differences that indicate potential benefits limited to areas located in warmer climates, where direct climate change impacts will be more discernible.
Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset
Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.