Reiner Schnur

Climate-carbon cycle feedback analysis: Results from the C4MIP model intercomparison

Eleven coupled climate–carbon cycle models used a common protocol to study the coupling between climate change and the carbon cycle. The models were forced by historical emissions and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 anthropogenic emissions of CO2 for the 1850–2100 time period. For each model, two simulations were performed in order to isolate the impact of climate change on the land and ocean carbon cycle, and therefore the climate feedback on the atmospheric CO2 concentration growth rate. There was unanimous agreement among the models that future climate change will reduce the efficiency of the earth system to absorb the anthropogenic carbon perturbation. A larger fraction of anthropogenic CO2 will stay airborne if climate change is accounted for. By the end of the twenty-first century, this additional CO2 varied between 20 and 200 ppm for the two extreme models, the majority of the models lying between 50 and 100 ppm. The higher CO2 levels led to an additional climate warming ranging between 0.1° and 1.5°C. All models simulated a negative sensitivity for both the land and the ocean carbon cycle to future climate. However, there was still a large uncertainty on the magnitude of these sensitivities. Eight models attributed most of the changes to the land, while three attributed it to the ocean. Also, a majority of the models located the reduction of land carbon uptake in the Tropics. However, the attribution of the land sensitivity to changes in net primary productivity versus changes in respiration is still subject to debate; no consensus emerged among the models.

Global Retrospective Estimation of Soil Moisture Using the Variable Infiltration Capacity Land Surface Model, 1980–93

Abstract A daily set of surface meteorological forcings, model-derived surface moisture fluxes, and state variables for global land areas for the period of 1979–93 is described. The forcing dataset facilitates global simulations and evaluation of land surface parameterizations without relying heavily on GCM output. Daily precipitation and temperature are based on station observations, daily wind speeds are based on National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis data, and the remaining meteorological forcing variables (shortwave radiation, longwave radiation, and vapor pressure) are derived from the precipitation and temperature series. The Variable Infiltration Capacity (VIC) land surface model is used to produce a set of derived fluxes and state variables, including snow water equivalent, evapotranspiration, runoff, and soil moisture storage. The main differences between the new dataset and other, similar datasets are the daily time step, the use of a sp...

Detecting and Attributing External Influences on the Climate System: A Review of Recent Advances

Abstract This paper reviews recent research that assesses evidence for the detection of anthropogenic and natural external influences on the climate. Externally driven climate change has been detected by a number of investigators in independent data covering many parts of the climate system, including surface temperature on global and large regional scales, ocean heat content, atmospheric circulation, and variables of the free atmosphere, such as atmospheric temperature and tropopause height. The influence of external forcing is also clearly discernible in reconstructions of hemispheric-scale temperature of the last millennium. These observed climate changes are very unlikely to be due only to natural internal climate variability, and they are consistent with the responses to anthropogenic and natural external forcing of the climate system that are simulated with climate models. The evidence indicates that natural drivers such as solar variability and volcanic activity are at most partially responsible fo...

Principal oscillation patterns: a review

Abstract The principal oscillation pattern (POP) analysis is a technique used to simultaneously infer the characteristic patterns and timescales of a vector time series. The POPs may be seen as the normal modes of a linearized system whose system matrix is estimated from data. The concept of POP analysis is reviewed. Examples are used to illustrate the potential of the POP technique. The best defined POPs of tropospheric day-to-day variability coincide with the most unstable modes derived from linearized theory. POPs can be derived even from a space-time subset of data. POPs are successful in identifying two independent modes with similar timescales in the same dataset. The POP method can also produce forecasts that may potentially be used as a reference for other forecast models. The conventional POP analysis technique has been generalized in various ways. In the cyclostationary POP analysis, the estimated system matrix is allowed to vary deterministically with an externally forced cycle. In the complex ...

Verification of GCM-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions

Empirical downscaling procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a downscaling technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical downscaling procedures in climate change applications. The case considered is regional seasonal precipitation in Romania. The downscaling model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). The climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in ‘‘time-slice’’ mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050. The downscaling model is skillful for all seasons except spring. The general features of the large-scale SLP variability are reproduced fairly well by both GCMs in all seasons. The climate models reproduce the empirically determined precipitation‐SLP link in winter, whereas the observed link is only partially captured for the other seasons. Thus, these models may be considered skillful with respect to regional precipitation during winter, and partially during the other seasons. Generally, applications of statistical downscaling to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 3 CO2 GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical downscaling. Since the skill of the GCMs in regional terms is already established, it is concluded that the downscaling technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.

Impact of terrestrial biosphere carbon exchanges on the anomalous CO2 increase in 2002–2003

Understanding the carbon dynamics of the terrestrial biosphere during climate fluctuations is a prerequisite for any reliable modeling of the climate-carbon cycle feedback. We drive a terrestrial vegetation model with observed climate data to show that most of the fluctuations in atmospheric CO2 are consistent with the modeled shift in the balance between carbon uptake by terrestrial plants and carbon loss through soil and plant respiration. Simulated anomalies of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) during the last two El Nino events also agree well with satellite observations. Our model results suggest that changes in net primary productivity (NPP) are mainly responsible for the observed anomalies in the atmospheric CO2 growth rate. Changes in heterotrophic respiration (Rh) mostly happen in the same direction, but with smaller amplitude. We attribute the unusual acceleration of the atmospheric CO2 growth rate during 2002-2003 to a coincidence of moderate El Nino conditions in the tropics with a strong NPP decrease at northern mid latitudes, only partially compensated by decreased Rh.

Normal Modes of the Atmosphere as Estimated by Principal Oscillation Patterns and Derived from Quasigeostrophic Theory

Abstract The principal oscillation pattern (POP) analysis is a technique to empirically identify time-dependent spatial patterns in a multivariate time series of geophysical or other data. In order to investigate medium-scale and synoptic waves in the atmosphere it has been applied to tropospheric geopotential height fields of ECMWF analyses from 1984 to 1987. The data have been subjected to zonal Fourier decomposition and to time filtering so that variations with periods between 3 and 25 days were retained. Analyses have been performed separately for each zonal wavenumber 5–9 on the Northern Hemisphere in winter and on the Southern Hemisphere in summer (DJF). P0Ps can be seen as normal modes of a linear approximation to a more complex dynamical system. The system matrix is estimated from observations of nature. This concept is compared with conventional stability analysis where the system matrix of the linear system is derived from theoretical, in this case quasigeostrophic, reasoning. Only the mean basi...

Climate science: The investment forecast

New studies predict that the risk of extreme rainfall over Europe and Asian monsoon regions is increasing, with more floods likely worldwide. Such long-range forecasting is pushing at the limit of current climate models.

The investment forecast.

New studies predict that the risk of extreme rainfall over Europe and Asian monsoon regions is increasing, with more floods likely worldwide. Such long-range forecasting is pushing at the limit of current climate models.

Separation of the Effects of Land and Climate Model Errors on Simulated Contemporary Land Carbon Cycle Trends in the MPI Earth System Model version 1

AbstractThe capacity of earth system models (ESMs) to make reliable projections of future atmospheric CO2 and climate is strongly dependent on the ability of the land surface model to adequately simulate the land carbon (C) cycle. Defining “adequate” performance of the land model requires an understanding of the contributions of climate model and land model errors to the land C cycle. Here, a benchmarking framework is applied based on significant, observed characteristics of the land C cycle for the contemporary period, for which sufficient evaluation data are available, to test the ability of the JSBACH land surface component of the Max Planck Institute Earth System Model (MPI-ESM) to simulate land C trends. Particular attention is given to the role of potential effects caused by climate biases, and therefore investigation is made of the results of model configurations in which JSBACH is interactively “coupled” to atmosphere and ocean components and of an “uncoupled” configuration, where JSBACH is driven...