Quantifying the role of biosphere-atmosphere feedbacks in climate change: coupled model simulation for 6000 years B.P. and comparison with palaeodata for northern Eurasia and northern Africa

The LMD AGCM was iteratively coupled to the global BIOME1 model in order to explore the role of vegetation-climate interactions in response to mid-Holocene (6000 y BP) orbital forcing. The sea-surface temperature and sea-ice distribution used were present-day and CO2 concentration was pre-industrial. The land surface was initially prescribed with present-day vegetation. Initial climate “anomalies” (differences between AGCM results for 6000 y BP and control) were used to drive BIOME1; the simulated vegetation was provided to a further AGCM run, and so on. Results after five iterations were compared to the initial results in order to identify vegetation feedbacks. These were centred on regions showing strong initial responses. The orbitally induced high-latitude summer warming, and the intensification and extension of Northern Hemisphere tropical monsoons, were both amplified by vegetation feedbacks. Vegetation feedbacks were smaller than the initial orbital effects for most regions and seasons, but in West Africa the summer precipitation increase more than doubled in response to changes in vegetation. In the last iteration, global tundra area was reduced by 25% and the southern limit of the Sahara desert was shifted 2.5 °N north (to 18 °N) relative to today. These results were compared with 6000 y BP observational data recording forest-tundra boundary changes in northern Eurasia and savana-desert boundary changes in northern Africa. Although the inclusion of vegetation feedbacks improved the qualitative agreement between the model results and the data, the simulated changes were still insufficient, perhaps due to the lack of ocean-surface feedbacks.

Simulation of long-term thermal characteristics of three Estonian lakes

A one-dimensional surface energy-balance lake model, coupled to a thermodynamic model of lake ice, is used to simulate variations in the temperature of and evaporation from three Estonian lakes: Karujärv, Viljandi and Kirjaku. The model is driven by daily climate data, derived by cubic-spline interpolation from monthly mean data, and was run for periods of 8 years (Kirjaku) up to 30 years (Viljandi). Simulated surface water temperature is in good agreement with observations: mean differences between simulated and observed temperatures are from −0.8°C to +0.1°C. The simulated duration of snow and ice cover is comparable with observed. However, the model generally underpredicts ice thickness and overpredicts snow depth. Sensitivity analyses suggest that the model results are robust across a wide range (0.1–2.0 m−1) of lake extinction coefficient: surface temperature differs by less than 0.5°C between extreme values of the extinction coefficient. The model results are more sensitive to snow and ice albedos. However, changing the snow (0.2–0.9) and ice (0.15–0.55) albedos within realistic ranges does not improve the simulations of snow depth and ice thickness. The underestimation of ice thickness is correlated with the overestimation of snow cover, since a thick snow layer insulates the ice and limits ice formation. The overestimation of snow cover results from the assumption that all the simulated winter precipitation occurs as snow, a direct consequence of using daily climate data derived by interpolation from mean monthly data.

Improved simulation of fire–vegetation interactions in the Land surface Processes and eXchanges dynamic global vegetation model (LPX-Mv1)

The Land surface Processes and eXchanges (LPX) model is a fire-enabled dynamic global vegetation model that performs well globally but has problems representing fire regimes and vegetative mix in savannas. Here we focus on improving the fire module. To improve the representation of ignitions, we introduced a reatment of lightning that allows the fraction of ground strikes to vary spatially and seasonally, realistically partitions strike distribution between wet and dry days, and varies the number of dry days with strikes. Fuel availability and moisture content were improved by implementing decomposition rates specific to individual plant functional types and litter classes, and litter drying rates driven by atmospheric water content. To improve water extraction by grasses, we use realistic plant-specific treatments of deep roots. To improve fire responses, we introduced adaptive bark thickness and post-fire resprouting for tropical and temperate broadleaf trees. All improvements are based on extensive analyses of relevant observational data sets. We test model performance for Australia, first evaluating parameterisations separately and then measuring overall behaviour against standard benchmarks. Changes to the lightning parameterisation produce a more realistic simulation of fires in southeastern and central Australia. Implementation of PFT-specific decomposition rates enhances performance in central Australia. Changes in fuel drying improve fire in northern Australia, while changes in rooting depth produce a more realistic simulation of fuel availability and structure in central and northern Australia. The introduction of adaptive bark thickness and resprouting produces more realistic fire regimes in Australian savannas. We also show that the model simulates biomass recovery rates consistent with observations from several different regions of the world characterised by resprouting vegetation. The new model (LPX-Mv1) produces an improved simulation of observed vegetation composition and mean annual burnt area, by 33 and 18% respectively compared to LPX.

The role of simulation in intertemporal choices

One route to understanding the thoughts and feelings of others is by mentally putting one's self in their shoes and seeing the world from their perspective, i.e., by simulation. Simulation is potentially used not only for inferring how others feel, but also for predicting how we ourselves will feel in the future. For instance, one might judge the worth of a future reward by simulating how much it will eventually be enjoyed. In intertemporal choices between smaller immediate and larger delayed rewards, it is observed that as the length of delay increases, delayed rewards lose subjective value; a phenomenon known as temporal discounting. In this article, we develop a theoretical framework for the proposition that simulation mechanisms involved in empathizing with others also underlie intertemporal choices. This framework yields a testable psychological account of temporal discounting based on simulation. Such an account, if experimentally validated, could have important implications for how simulation mechanisms are investigated, and makes predictions about special populations characterized by putative deficits in simulating others.

Exploring the impact of CMIP5 model biases on the simulation of North Atlantic decadal variability

Instrumental observations, palaeo-proxies, and climate models suggest significant decadal variability within the North Atlantic subpolar gyre (NASPG). However, a poorly sampled observational record and a diversity of model behaviours mean that the precise nature and mechanisms of this variability are unclear. Here, we analyse an exceptionally large multi-model ensemble of 42 present-generation climate models to test whether NASPG mean state biases systematically affect the representation of decadal variability. Temperature and salinity biases in the Labrador Sea co-vary and influence whether density variability is controlled by temperature or salinity variations. Ocean horizontal resolution is a good predictor of the biases and the location of the dominant dynamical feedbacks within the NASPG. However, we find no link to the spectral characteristics of the variability. Our results suggest that the mean state and mechanisms of variability within the NASPG are not independent. This represents an important caveat for decadal predictions using anomaly-assimilation methods.

Assessing the accuracy and dispersion of real estate investment forecasts

Existing empirical evidence has frequently observed that professional forecasters are conservative and display herding behaviour. Whilst a large number of papers have considered equities as well as macroeconomic series, few have considered the accuracy of forecasts in alternative asset classes such as real estate. We consider the accuracy of forecasts for the UK commercial real estate market over the period 1999-2011. The results illustrate that forecasters display a tendency to under-estimate growth rates during strong market conditions and over-estimate when the market is performing poorly. This conservatism not only results in smoothed estimates but also implies that forecasters display herding behaviour. There is also a marked difference in the relative accuracy of capital and total returns versus rental figures. Whilst rental growth forecasts are relatively accurate, considerable inaccuracy is observed with respect to capital value and total returns.

Future changes in the Western North Pacific tropical cyclone activity projected by a multidecadal simulation with a 16-km global atmospheric GCM

How tropical cyclone (TC) activity in the northwestern Pacific might change in a future climate is assessed using multidecadal Atmospheric Model Intercomparison Project (AMIP)-style and time-slice simulations with the ECMWF Integrated Forecast System (IFS) at 16-km and 125-km global resolution. Both models reproduce many aspects of the present-day TC climatology and variability well, although the 16-km IFS is far more skillful in simulating the full intensity distribution and genesis locations, including their changes in response to El Niño–Southern Oscillation. Both IFS models project a small change in TC frequency at the end of the twenty-first century related to distinct shifts in genesis locations. In the 16-km IFS, this shift is southward and is likely driven by the southeastward penetration of the monsoon trough/subtropical high circulation system and the southward shift in activity of the synoptic-scale tropical disturbances in response to the strengthening of deep convective activity over the central equatorial Pacific in a future climate. The 16-km IFS also projects about a 50% increase in the power dissipation index, mainly due to significant increases in the frequency of the more intense storms, which is comparable to the natural variability in the model. Based on composite analysis of large samples of supertyphoons, both the development rate and the peak intensities of these storms increase in a future climate, which is consistent with their tendency to develop more to the south, within an environment that is thermodynamically more favorable for faster development and higher intensities. Coherent changes in the vertical structure of supertyphoon composites show system-scale amplification of the primary and secondary circulations with signs of contraction, a deeper warm core, and an upward shift in the outflow layer and the frequency of the most intense updrafts. Considering the large differences in the projections of TC intensity change between the 16-km and 125-km IFS, this study further emphasizes the need for high-resolution modeling in assessing potential changes in TC activity.

Spatial evaluation of volcanic ash forecasts using satellite observations

The decision to close airspace in the event of a volcanic eruption is based on hazard maps of predicted ash extent. These are produced using output from volcanic ash transport and dispersion (VATD)models. In this paper an objectivemetric to evaluate the spatial accuracy of VATD simulations relative to satellite retrievals of volcanic ash is presented. The 5 metric is based on the fractions skill score (FSS). Thismeasure of skill provides more information than traditional point-bypoint metrics, such as success index and Pearson correlation coefficient, as it takes into the account spatial scale overwhich skill is being assessed. The FSS determines the scale overwhich a simulation has skill and can differentiate between a "near miss" and a forecast that is badly misplaced. The 10 idealised scenarios presented show that even simulations with considerable displacement errors have useful skill when evaluated over neighbourhood scales of 200–700km2. This method could be used to compare forecasts produced by different VATDs or using different model parameters, assess the impact of assimilating satellite retrieved ash data and evaluate VATD forecasts over a long time period.

Atmospheric impact of Arctic Sea ice loss in a coupled ocean–atmosphere simulation

The atmospheric response to an idealized decline in Arctic sea ice is investigated in a novel fully coupled climate model experiment. In this experiment two ensembles of single-year model integrations are performed starting on 1 April, the approximate start of the ice melt season. By perturbing the initial conditions of sea ice thickness (SIT), declines in both sea ice concentration and SIT, which result in sea ice distributions that are similar to the recent sea ice minima of 2007 and 2012, are induced. In the ice loss regions there are strong (~3 K) local increases in sea surface temperature (SST); additionally, there are remote increases in SST in the central North Pacific and subpolar gyre in the North Atlantic. Over the central Arctic there are increases in surface air temperature (SAT) of ~8 K due to increases in ocean–atmosphere heat fluxes. There are increases in SAT over continental North America that are in good agreement with recent changes as seen by reanalysis data. It is estimated that up to two-thirds of the observed increase in SAT in this region could be related to Arctic sea ice loss. In early summer there is a significant but weak atmospheric circulation response that projects onto the summer North Atlantic Oscillation (NAO). In early summer and early autumn there is an equatorward shift of the eddy-driven jet over the North Atlantic as a result of a reduction in the meridional temperature gradients. In winter there is no projection onto a particular phase of the NAO.

Modelling spectral reflectance of open cork oak woodland: a simulation analysis of the effects of vegetation structure and background

This study analyses the influence of vegetation structure (i.e. leaf area index and canopy cover) and seasonal background changes on moderate-resolution imaging spectrometer (MODIS)-simulated reflectance data in open woodland. Approximately monthly spectral reflectance and transmittance field measurements (May 2011 to October 2013) of cork oak tree leaves (Quercus suber) and of the herbaceous understorey were recorded in the region of Ribatejo, Portugal. The geometric-optical and radiative transfer (GORT) model was used to simulate MODIS response (red, near-infrared) and to calculate vegetation indices, investigating their response to changes in the structure of the overstorey vegetation and to seasonal changes in the understorey using scenarios corresponding to contrasting phenological status (dry season vs. wet season). The performance of normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) is discussed. Results showed that SAVI and EVI were very sensitive to the emergence of background vegetation in the wet season compared to NDVI and that shading effects lead to an opposing trend in the vegetation indices. The information provided by this research can be useful to improve our understanding of the temporal dynamic of vegetation, monitored by vegetation indices.