Local ecosystem feedbacks and critical transitions in the climate

Global and regional climate models, such as those used in IPCC assessments, are the best tools available for climate predictions. Such models typically account for large-scale land-atmosphere feedbacks. However, these models omit local vegetationenvironment 5 feedbacks that are crucial for critical transitions in ecosystems. Here, we reveal the hypothesis that, if the balance of feedbacks is positive at all scales, local vegetation-environment feedbacks may trigger a cascade of amplifying effects, propagating from local to large scale, possibly leading to critical transitions in the largescale climate. We call for linking local ecosystem feedbacks with large-scale land10 atmosphere feedbacks in global and regional climate models in order to yield climate predictions that we are more confident about

Global soil moisture bimodality in satellite observations and climate models

A new diagnostic metric based on soil moisture bimodality is developed in order to examine and compare soil moisture from satellite observations and Earth System Models. The methodology to derive this diagnostic is based on maximum likelihood estimator encoded into an iterative algorithm, which is applied to the soil moisture probability density function. This metric is applied to satellite data from the Advanced Microwave Scanning Radiometer for the Earth Observing System and global climate models data from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Results show high soil moisture bimodality in transitional climate areas and high latitudes, potentially associated with land-atmosphere feedback processes. When comparing satellite versus climate models, a clear difference in their soil moisture bimodality is observed, with systematically higher values in the case of CMIP5 models. These differences appear related to areas where land-atmospheric feedback may be overestimated in current climate models

Evaluation of different sources of uncertainty in climate change impact research using a hydro-climatic model ensemble

The international research project QBic3 (Quebec-Bavarian Collaboration on Climate Change) aims at investigating the potential impacts of climate change on the hydrology of regional scale catchments in Southern Quebec (Canada) and Bavaria (Germany). Yet, the actual change in river runoff characteristics during the next 70 years is highly uncertain due to a multitude of uncertainty sources. The so-called hydro-climatic ensemble that is constructed to describe the uncertainties of this complex model chain consists of four different global climate models, downscaled by three different regional climate models, an exchangeable bias correction algorithm, a separate method to scale RCM outputs to the hydrological model scale and several hydrological models of differing complexity to assess the impact of different hydro model concepts. This choice of models and scenarios allows for the inter-comparison of the uncertainty ranges of climate and hydrological models, of the natural variability of the climate system as well as of the impact of scaling and correction of climate data on mean, high and low flow conditions. A methodology to display the relative importance of each source of uncertainty is proposed and results for past runoff and potential future changes are presented

Issues Related to Incorporating Northern Peatlands into Global Climate Models

Northern peatlands cover ~3–4 million km2 (~10% of the land north of 45°N) and contain ~200–400 Pg carbon (~10–20% of total global soil carbon), almost entirely as peat (organic soil). Recent developments in global climate models have included incorporation of the terrestrial carbon cycle and representation of several terrestrial ecosystem types and processes in their land surface modules. Peatlands share many general properties with upland, mineral-soil ecosystems, and general ecosystem carbon, water, and energy cycle functions (productivity, decomposition, water infiltration, evapotranspiration, runoff, latent, sensible, and ground heat fluxes). However, northern peatlands also have several unique characteristics that will require some rethinking or revising of land surface algorithms in global climate models. Here we review some of these characteristics, deep organic soils, a significant fraction of bryophyte vegetation, shallow water tables, spatial heterogeneity, anaerobic biogeochemistry, and disturbance regimes, in the context of incorporating them into global climate models. With the incorporation of peatlands, global climate models will be able to simulate the fate of northern peatland carbon under climate change, and estimate the magnitude and strength of any climate system feedbacks associated with the dynamics of this large carbon pool

Benefits of greenhouse gas mitigation on the supply, management, and use of water resources in the United States

Climate change impacts on water resources in the United States are likely to be far-reaching and substantial because the water is integral to climate, and the water sector spans many parts of the economy. This paper estimates impacts and damages from five water resource-related models addressing runoff, drought risk, economics of water supply/demand, water stress, and flooding damages. The models differ in the water system assessed, spatial scale, and unit of assessment, but together provide a quantitative and descriptive richness in characterizing water sector effects that no single model can capture. The results, driven by a consistent set of greenhouse gas (GHG) emission and climate scenarios, examine uncertainty from emissions, climate sensitivity, and climate model selection. While calculating the net impact of climate change on the water sector as a whole may be impractical, broad conclusions can be drawn regarding patterns of change and benefits of GHG mitigation. Four key findings emerge: 1) GHG mitigation substantially reduces hydro-climatic impacts on the water sector; 2) GHG mitigation provides substantial national economic benefits in water resources related sectors; 3) the models show a strong signal of wetting for the Eastern US and a strong signal of drying in the Southwest; and 4) unmanaged hydrologic systems impacts show strong correlation with the change in magnitude and direction of precipitation and temperature from climate models, but managed water resource systems and regional economic systems show lower correlation with changes in climate variables due to non-linearities created by water infrastructure and the socio-economic changes in non-climate driven water demand

Comparing the Model-simulated Global Warming Signal to Observations Using Empirical Estimates of Unforced Noise

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20th century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario’s forced signal, but is likely inconsistent with the steepest emission scenario’s forced signal

Factors controlling the bifurcation structure of sea ice retreat

The contrast in surface albedo between sea ice and open ocean suggests the possibility of an unstable climate state flanked by two separate stable climate states. Previous studies using idealized single-column models and comprehensive climate models have considered the possibility of abrupt thresholds during sea ice retreat associated with such multiple states, and they have produced a wide range of results. When the climate is warmed such that the summer minimum Arctic sea ice cover reaches zero, some models smoothly transition to seasonally ice-free conditions, others discontinuously transition to seasonally ice-free conditions, and others discontinuously transition to annually ice-free conditions. Among the models that simulate a continuous transition to seasonally ice-free conditions, further warming causes some to smoothly lose the remaining wintertime-only sea ice cover and others to discontinuously lose it. Here, we use a toy model representing the essential physics of thermodynamic sea ice in a single column to investigate the factors controlling which of these scenarios occurs. All of the scenarios are shown to be possible in the toy model when the parameters are varied, and physical mechanisms giving rise to each scenario are investigated. We find that parameter shifts that make ice thicker or open ocean warmer under a given climate forcing make models less prone to stable seasonally ice-free conditions and more prone to bistability and hence bifurcations. The results are used to interpret differences in simulated sea ice stability in comprehensive climate models

The match between climate services demands and Earth System Models supplies

Earth System Models (ESM) are key ingredients of many of the climate services that are currently being developed and delivered. However, ESMs have more applications than the provision of climate services, and similarly many climate services use more sources of information than ESMs. This discussion paper elaborates on dilemmas that are evident at the interface between ESMs and climate services, in particular: (a) purposes of the models versus service development, (b) gap between the spatial and temporal scales of the models versus the scales needed in applications, and (c) Tailoring climate model results to real-world applications. A continued and broad-minded dialogue between the ESM developers and climate services providers’ communities is needed to improve both the optimal use and direction of ESM development and climate service development. We put forward considerations to improve this dialogue between the communities developing ESMs and climate services, in order to increase the mutual benefit that enhanced understanding of prospects and limitations of ESMs and climate services will bring.This work and its contributors (B. van den Hurk, C. Hewitt, J. Bessembinder, F. Doblas-Reyes, R. Döscher) were funded by the Horizon 2020 Framework Programme of the European Union: Project ref. 689029 (Climateurope project). The co-author and editor of the journal states that she was not involved in the review process of the paper.Peer ReviewedPostprint (published version

Using proper divergence functions to evaluate climate models

It has been argued persuasively that, in order to evaluate climate models, the probability distributions of model output need to be compared to the corresponding empirical distributions of observed data. Distance measures between probability distributions, also called divergence functions, can be used for this purpose. We contend that divergence functions ought to be proper, in the sense that acting on modelers' true beliefs is an optimal strategy. Score divergences that derive from proper scoring rules are proper, with the integrated quadratic distance and the Kullback-Leibler divergence being particularly attractive choices. Other commonly used divergences fail to be proper. In an illustration, we evaluate and rank simulations from fifteen climate models for temperature extremes in a comparison to re-analysis data