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

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

Can subsidizing alternative energy technology development lead to faster global warming?

Modelling global climate changes without taking account of the changes in resource markets can produce climate policy with perverse consequences. In even the simplest economic model of emissions of greenhouse gases, naïve policies that ignore markets can lead to perverse outcomes- the opposite of that intended by the policymakers-such as accelerating global warming. Yet the global climate models that are commonly used to develop climate policy do not adequately model resource markets. As a consequence, we need to develop better models of resource markets within our global climate models.Environmental Economics and Policy,

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

The Importance of Ice Vertical Resolution for Snowball Climate and Deglaciation

Sea ice schemes with a few vertical levels are typically used to simulate the thermodynamic evolution of sea ice in global climate models. Here it is shown that these schemes overestimate the magnitude of the diurnal surface temperature cycle by a factor of 2–3 when they are used to simulate tropical ice in a Snowball earth event. This could strongly influence our understanding of Snowball termination, which occurs in global climate models when the midday surface temperature in the tropics reaches the melting point. A hierarchy of models is used to show that accurate simulation of surface temperature variation on a given time scale requires that a sea ice model resolve the e-folding depth to which a periodic signal on that time scale penetrates. This is used to suggest modifications to the sea ice schemes used in global climate models that would allow more accurate simulation of Snowball deglaciation

Uncertainties in climate change projections and regional downscaling: implications for water resources management

Climate change is expected to have a large impact on water resources worldwide. A major problem in assessing the potential impact of a changing climate on these resources is the difference in spatial scale between available climate change projections and water resources management. Regional climate models (RCMs) are often used for the spatial disaggregation of the outputs of global circulation models. However, RCMs are time-intensive to run and typically only a small number of model runs is available for a certain region of interest. This paper investigates the value of the improved representation of local climate processes by a regional climate model for water resources management in the tropical Andes of Ecuador. This region has a complex hydrology and its water resources are under pressure. Compared to the IPCC AR4 model ensemble, the regional climate model PRECIS does indeed capture local gradients better than global models, but locally the model is prone to large discrepancies between observed and modelled precipitation. It is concluded that a further increase in resolution is necessary to represent local gradients properly. Furthermore, to assess the uncertainty in downscaling, an ensemble of regional climate models should be implemented. Finally, translating the climate variables to streamflow using a hydrological model constitutes a smaller but not negligible source of uncertainty

Estimating the maximum rise in temperature according to climate models using abstract interpretation

Current climate models are complex computer programs that are typically iterated time-step by time-step to predict the next set of values of the climate-related variables. Since these iterative methods are necessarily computed only for a fixed number of iterations, they are unable to answer the natural question whether there is a limit to the rise of global temperature. In order to answer that question we propose to combine climate models with software verification techniques that can find invariant conditions for the set of program variables. In particular, we apply the constraint database approach to software verification to find that the rise in global temperature is bounded according to the common Java Climate Model that implements the Wigely/Raper Upwelling-Diffusion Energy Balance Model climate model

Evolution of the Dynamic Integrated Climate-Economy (DICE) model : decomposition of changes over time

Global warming is one of the major environmental challenges of the modern era. Global temperature in 2005 has increased about 0.7°C (1.3°F) compare to 1900, also CO₂ concentrations increased by 100 parts per million (ppm). Estimated expense to decrease the CO₂ concentrations by 1 ppm is about 1 trillion (Pielke, 2009). Overcoming global warming is difficult because it is interdisciplinary problem and involves many parts of society. Any proposed policies must balance the economic costs of operations today and future corresponding economic and environmental benefits. There are several studies and models which used economics and mathematical modeling to analyzed the efficiency of different approaches and policies to slowing global warming. Dynamic Integrated model of Climate and the Economy (DICE) model uses economics and mathematical modeling to analyzed the efficiency of different approaches and policies to slow global warming (William D. Nordhaus, 1994, 2008, 2017, 2017a; William D. Nordhaus & Boyer, 2000). The main distinguishing feature of the DICE model is connecting economy and climate change factors including the carbon cycle, radiative forcing equation, climate change equations, and climate damage relationship. DICE finds optimal emissions control rate by balancing abatement costs of reducing emissions, and economic growth due to avoiding future climate damages. DICE-2016 shows following results under optimal emissions reduction policy, emissions reduction rate for CO₂ is increasing to 36 percent by 2050 and 84 percent by 2100 relative to the baseline. Corresponding, CO₂ concentrations is decreased and increase in global temperature relative to 1900 is decreased to 6.17°F (3.43°C) for 2100 and 6.96°F (3.87°C) for 2200. The net present value abatement cost and climate damages of the optimal policy is 42.6 trillion beneficial relative to no control. This includes $20.4 trillion of abatement costs and$63 trillion of reduced climatic damages. There is still $81.8 trillion climate damage even after taking optimal policy. We compared the outputs of DICE-2016 and DICE-2007 to understand the economic effect of climate change and how climate changing is modeled in these two models. By comparing these models, we obtained estimated economical abatement costs to reduce emissions, social cost of carbon, and impact of climate change and global warming on the economy. We were trying to identify which changes have the most effect on the difference between these two models.Operations Research and Industrial Engineerin