Matthias Mengel

Multisectoral climate impact hotspots in a warming world

The impacts of global climate change on different aspects of humanity’s diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 °C above the 1980–2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 °C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty.

Future sea level rise constrained by observations and long-term commitment

Significance Anthropogenic sea level rise poses challenges to coastal areas worldwide, and robust projections are needed to assess mitigation options and guide adaptation measures. Here we present an approach that combines information about the equilibrium sea level response to global warming and last centurys observed contribution from the individual components to constrain projections for this century. This “constrained extrapolation” overcomes limitations of earlier global semiempirical estimates because long-term changes in the partitioning of total sea level rise are accounted for. While applying semiempirical methodology, our method yields sea level projections that overlap with the process-based estimates of the Intergovernmental Panel on Climate Change. The method can thus lead to a better understanding of the gap between process-based and global semiempirical approaches. Sea level has been steadily rising over the past century, predominantly due to anthropogenic climate change. The rate of sea level rise will keep increasing with continued global warming, and, even if temperatures are stabilized through the phasing out of greenhouse gas emissions, sea level is still expected to rise for centuries. This will affect coastal areas worldwide, and robust projections are needed to assess mitigation options and guide adaptation measures. Here we combine the equilibrium response of the main sea level rise contributions with their last centurys observed contribution to constrain projections of future sea level rise. Our model is calibrated to a set of observations for each contribution, and the observational and climate uncertainties are combined to produce uncertainty ranges for 21st century sea level rise. We project anthropogenic sea level rise of 28–56 cm, 37–77 cm, and 57–131 cm in 2100 for the greenhouse gas concentration scenarios RCP26, RCP45, and RCP85, respectively. Our uncertainty ranges for total sea level rise overlap with the process-based estimates of the Intergovernmental Panel on Climate Change. The “constrained extrapolation” approach generalizes earlier global semiempirical models and may therefore lead to a better understanding of the discrepancies with process-based projections.

A Scaling Approach to Probabilistic Assessment of Regional Climate Change

AbstractA new approach to probabilistic projections of regional climate change is introduced. It builds on the already established quasi-linear relation between global-mean temperature and regional climate change found in atmosphere–ocean general circulation models (AOGCMs). The new approach simultaneously 1) takes correlations between temperature- and precipitation-related uncertainty distributions into account, 2) enables the inclusion of predictors other than global-mean temperature, and 3) checks for the interscenario and interrun variability of the scaling relationships. This study tests the effectiveness of SOx and black carbon emissions and greenhouse gas forcings as additional predictors of precipitation changes. The future precipitation response is found to deviate substantially from the linear relationship with global-mean temperature change in some regions; thereby, the two main limitations of a simple linear scaling approach, namely having to rely on exogenous aerosol experiments (or ignoring ...

Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action

Sea-level rise is a major consequence of climate change that will continue long after emissions of greenhouse gases have stopped. The 2015 Paris Agreement aims at reducing climate-related risks by reducing greenhouse gas emissions to net zero and limiting global-mean temperature increase. Here we quantify the effect of these constraints on global sea-level rise until 2300, including Antarctic ice-sheet instabilities. We estimate median sea-level rise between 0.7 and 1.2 m, if net-zero greenhouse gas emissions are sustained until 2300, varying with the pathway of emissions during this century. Temperature stabilization below 2 °C is insufficient to hold median sea-level rise until 2300 below 1.5 m. We find that each 5-year delay in near-term peaking of CO2 emissions increases median year 2300 sea-level rise estimates by ca. 0.2 m, and extreme sea-level rise estimates at the 95th percentile by up to 1 m. Our results underline the importance of near-term mitigation action for limiting long-term sea-level rise risks.Rising seas are a legacy of present and future climate change. Here the authors show that under the Paris Agreement, emissions in the next decades have a strong influence on the amount of sea level rise in the centuries to come, with the uncertainty dominated by ice-sheet contributions.

Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0

ion is projected to increase to around 50% by 2100 (Wada, 2015). This indicates that, ultimately, the total amount of groundwater available for abstraction is limited. To account for such an upper bound of the LWS sea level contribution, we use a term that relates the cumulative LWS contribution to a theoretical maximum LWS volume that can be depleted. No distinction is made between different climate scenarios for the post-2100 LWS extension due to the limited process understanding and the associated large uncertainties (Church et al., 2013a). Hence, we implement the revised Wada et al. (2012) estimates until 2100 5 and apply the following post-2100 LWS parameterization: LWSt = LWSt−1 +LWSconst ( 1− LWSt−1 −LWS2100 LWSmax−LWS2100 )0.5 (9) The maximum LWS volume LWSmax has not been quantified yet de Graaf et al. (2014). However, Gleeson et al. (2015) quantified the amount of modern groundwater which is defined as less than 50 year old groundwater located in the top 2 km of the continental crust. This type of groundwater dominates the interaction with general hydrological cycle and the climate 10 system. It is also the most accessible for land use (Gleeson et al., 2015). We here define LWSmax as the total amount of available modern groundwater which has been estimated to be around 350,000 km, roughly translating to 1000 mm SLE. 2.6 Model calibration For the MAGICC ocean model calibration, we use two CMIP5 variables for our reference data set: ocean potential temperatures (thetao) and thermal expansion (zostoga). Ocean depths specific thetao time series are extracted for a total of 36 CMIP5 models 15 which have been running pre-industrial control (pictrl), historical, some or all of the RCP experiments as well as the idealized 1% CO2 per year increase (1pctCO2) experiments. Each individual model output is converted into hemispheric annual mean thetao depth profile time series that are then vertically interpolated to match the MAGICC ocean layer depths. We combine historical and RCP runs to create layer-specific time series from 1850 to 2100 or 2300 depending on the experiment lengths of the individual CMIP5 model runs. Ocean temperature data available from the CMIP archives are subject to drift because the 20 time scales for the ocean to adjust to external forcing are much longer than the length of the control experiments (Taylor et al., 2012; Gupta et al., 2013). Individual model drifts have been identified based on the respective pictrl runs. The full linear trend from the pictrl experiments has been removed from the historical plus RCP and 1pctCO2 scenario time series. The initial thetao profiles are prescribed for every CMIP5 model calibration as well as the respective depth-dependent ocean area fractions. We incorporate zostoga estimates for each of the 36 CMIP5 ensemble members by detrending the times series 25 with the full linear trend of the pictrl runs. To ensure a full CMIP5-consistent calibration setup, we constrain MAGICC for every CMIP5 model optimization by prescribing the corresponding model-specific annual global mean surface air temperature tas. Previous studies have shown that calibration methods for highly parameterized simple models do successfully show global convergence, even with a large number of free parameters (Hargreaves and Annan, 2002; Meinshausen et al., 2011a). Here, we select all MAGICC parameters which directly determine the ocean-layer specific potential ocean temperature and 30 corresponding thermal expansion responses. These 9 parameters drive the band-routine of the hemispheric upwelling-diffusion ocean model. The vertical thermal diffusivity,Kz , its sensitivity to global-mean surface temperatures at the mixed layer boundary, dKztop dT , the sea-ice adjustment parameters η and γ described above, the initial upwelling rate w0, the ratio of changes in 11 the temperature of the entraining waters to those of the polar sinking waters β, the ratio of variable to fixed upwelling for every time step ∆wt wt , and the corresponding threshold temperatures that lead to constant upwelling rates, namely Twt , and the global thermal expansion scaling coefficient φ. More details on the individual parameters can be found in Meinshausen et al. (2011a) except for the sea-ice adjustment variables described in Section 2.1. For every CMIP5 model, this suite of calibration parameters is optimized based on the scenario specific CMIP5 thetao data for the representative layers 1 (30m layer mean 5 depth), 2 (110m), 3(210m), 8 (710m), 15 (1410m), 30 (2910m), and 40 (3910m), and the corresponding zostoga time series. The eight calibration layers have been selected to allow the MAGICC ocean model to emulate the key features of the CMIP5 ocean temperature profiles, with the majority of calibration layers set in the upper ocean to ensure sufficient coverage of the stronger temperature gradients. The number of reference layers is not increased further to preserve computational efficiency. 5000 random parameter sets are drawn prior to each model optimization procedure. The number of initial random runs has 10 been determined through iterative testing to ensure convergence to a global optimum. The resulting best fit is subsequently used for the initialization of the automated Nelder-Mead simplex optimization routine (Lagarias et al., 1998; Nelder and Mead, 1965) with a termination tolerance of 10−8 and a maximum iteration number of 10,000. We use weighted Residual Sum of Squares (RSS) for Goodness-Of-Fit (GOF) diagnostics during the optimization process (Meinshausen et al., 2011a). The ocean calibration also takes into account the available CMIP5 zostoga time series. The zostoga optimization component is given four 15 orders of magnitude less relative weight than the thetao component in order to prioritize the accurate layer-by-layer emulation of the respective CMIP5 model thetao time series. The GOF values are then divided by the number of calibrated model years, accounting for the varying amount of scenario data available for each model. This allows us to compare the GOFs of the calibrations for all 36 CMIP5 models. The calibration procedures for the other SLR components also optimize the specific parameters listed in Tables 2 to 5 based 20 on the Nelder-Mead Simplex method with a termination tolerance of 10−8 for a change in RSS during the last iteration. For an overview of all relevant variables and calibration parameters please see Table A.1. All the remaining SLR components use reference SLE contributions in millimeters for the respective optimizations. For the glacier contribution, the MAGICC sea level response is fitted to the transient Marzeion et al. (2014) projections. The free parameters κ and ν are calibrated for each of the 14 CMIP5 reference models and their respective combined historical and RCP simulations, starting in 1850. Corresponding 25 CMIP5 global mean tas projections are prescribed in the MAGICC model to ensure consistency with CMIP5. We use a subset of the model specific 1965-2100 projections made available by Fettweis et al. (2013) to calibrate the parameterization for the Greenland SMB contribution. 24 CMIP5 models are selected based on the availability of CMIP5 tas projections for the scenarios RCP4.5 and RCP8.5. We then prescribe these global mean tas time series for the calibration procedure of the three parameters υ, χ, and φ. Calibration data for the Greenland SID component is only available for one GCM, ECHAM5. For 30 the optimization of the parameters %, , and GIS max , global mean tas runs for SRES A1B and RCP8.5 are used with 2200 extensions, repeating the last decade of the 21st century ten times (Nick et al., 2013). The calibration of the Antarctic SMB component is based on process-based SLR responses forced by two GCMs (Ligtenberg et al., 2013). In this reference study, ECHAM5 and HadCM3 model output was applied for scenarios SRES A1B and ENSEMBLES E1. We replicate these GCM responses and use the provided Antarctic SMB sea level contributions starting in 1980 to determine the optimal parameters 35 12 ξ, ρ, σ. The Antarctic SID as well as the LWS components are not subject to calibration procedures as they apply the same method of the reference study in the case of Antarctic SID or simply include and extend the reference data for LWS. 3 Results The MAGICC ocean model update yields optimal parameter sets for every CMIP5 model used in the calibration procedure outlined above. Those sets are listed in Table 1. In Figure 2, we show both the 90% model range and the median for the 5 reference CMIP5 global potential ocean temperature anomalies as well as the median MAGICC global ocean warming profile averaged over 2081 to 2100 relative to the reference period 1986 to 2005. The Figure also provides information on individual model outliers for reference data and calibration results. Corresponding potential ocean temperature residuals are shown in Figure A.1. MAGICC is able to capture the key CMIP5 features for all RCP scenarios. The median model response either matches or is close to the median of the CMIP5 responses. The updated MAGICC ocean deviates from the CMIP5 data in 10 a few cases. Generally, there appears to be less warming in the mid-ocean between around 1500 m and 2500 m than in the CMIP5 reference data. Also, there is a tendency for the MAGICC bottom layers to warm more than the CMIP5 reference data. However, it is only for two of the 36 CMIP5 models used that calibration results show a major bottom layer warming bias. The GISS-E2-R reference data show strong mid-layer warming combined with actual bottom layer cooling, while the HadGEM2-CC data show cooling in the upper 500 m over the historical period (see Figure A.2). In both cases, the MAGICC 15 hemispheric upwelling-diffusion ocean model cannot fully capture these characteristics. For the HadGEM2-CC emulation, MAGICC overcompensates the surface cooling with strong bottom layer warming. Apart from these anomalies,