News Article | December 9, 2015
The CMIP5 models29 analysed here are listed in Extended Data Table 1, with corresponding references. We use one ensemble member (r1i1p1) from 25 models that had available monthly output of atmospheric temperature and humidity, precipitation, and energy fluxes for the pre-industrial control (piControl) and abrupt quadrupled CO (abrupt4 × CO ) experiments at the time of analysis. The Gregory method10, 20 is employed using each model’s piControl and abrupt4 × CO run. For each year of the abrupt4 × CO run, the global-mean abrupt4 × CO anomaly of a physical quantity (for example, L P) relative to the piControl simulation is paired against the corresponding anomaly of 2-m air temperature, generating a scatterplot (Extended Data Fig. 1). To compute the annual anomalies, the piControl 21-year mean, centred on the corresponding year of the abrupt4 × CO simulation, is subtracted from each abrupt4 × CO 1-year mean. Subtracting this running mean removes possible influences of climate model drift on the anomalies. The scatterplots are generated using 150 years of the abrupt4 × CO simulation when available (140 years are available from the CNRM-CM-2 and IPSL-CM5A-MR models). A least-squares linear regression is then applied to each scatterplot, with the slope and y-intercept of the fit representing the temperature-mediated and rapid responses to CO forcing, respectively. The Gregory methodology is displayed visually for the GFDL-CM3 model in Extended Data Fig. 1. It shows the yearly evolution of globally averaged LWC, SWA, SH, and L P changes after a quadrupling of atmospheric CO . The physical interpretation of these changes has been thoroughly discussed in numerous studies6, 12, 16, 18, 30, 31, 32. The high degree of linearity of the scatterplots demonstrates the reliability of this approach for separating temperature-mediated responses and rapid adjustments. As shown in Fig. 2, model spread in the temperature-mediated SWA response, dSWA/dT, particularly for clear-sky, substantially contributes to the spread in L dP/dT. The total change in L P per unit warming (L ΔP/ΔT, which includes the temperature-mediated response and rapid adjustment), however, is not correlated with the corresponding total change in SWA across models (Extended Data Fig. 3a). This may be the consequence of large scatter in the rapid adjustment of L P, which is not strongly related to that of SWA (Extended Data Fig. 3b). Reference 7 found the opposite result using eight CMIP3 models: that the temperature-mediated responses of latent heating and solar absorption are not correlated, but that total changes at a warming of 2 K are anti-correlated (r = −0.66). The differences between the results in ref. 7 and ours probably originates from different analysis methods and sampling. For example, in ref. 7 only 8 models from CMIP3 were used, whereas we use 25 from CMIP5. Additionally, ref. 7 combined different types of CO forcing scenarios (for example, 1% increase to CO doubling or quadrupling and instantaneous CO doubling) and based their temperature-mediated estimates on scatterplots of relatively few (5–10) data points from each simulation. It is shown in their analysis that quite different slopes may be obtained from different forcing simulations even with the same model. We argue that our results are more robust. In addition to using many more models, we base our temperature-mediated estimates on a large number of data points (140–150) from a single forcing scenario (instantaneous CO quadrupling). Furthermore, our findings are consistent with results reported in ref. 4, where it was also found that total changes in precipitation (per unit warming) are not correlated with those in shortwave absorption under doubled CO using a larger sample of CMIP3 models (14) than used in ref. 7. Radiative kernels were developed using an offline version of the MPI-ECHAM5 radiation code and represent the sensitivity of top-of-atmosphere (TOA) and surface shortwave radiative fluxes to small perturbations in atmospheric specific humidity and surface albedo14. Global-mean temperature-mediated SWA responses due to water vapour changes are computed as follows: (1) temperature-mediated responses in the logarithm of specific humidity at all months, locations and pressure levels are multiplied by the shortwave specific humidity atmospheric (TOA minus surface) kernel for clear-sky. Global-annual-mean kernel values are shown in Extended Data Fig. 4a. Specific humidity responses are computed as the difference in abrupt4 × CO —piControl anomalies averaged over years 121–150 and 1–30 of the abrupt4 × CO simulation, normalized by the corresponding difference in 2-m air temperature. (2) The product is integrated over the depth of the troposphere (defined as all levels between the surface and a tropopause height that varies linearly with latitude from 100 hPa on the Equator to 300 hPa at the poles), and then averaged over all months and locations. Temperature-mediated SWA responses due to surface albedo are computed in similar fashion but with surface albedo kernels and temperature-mediated responses. The final kernel-derived temperature-mediated SWA response for each model (dSWA/dT(k) in Fig. 2a) is the sum of the water vapour and surface albedo components of the response, with the vapour component dominating14. It should be noted that the computation of atmospheric radiative feedbacks using kernels is associated with uncertainty12, 14. In this study, our goal is not to obtain perfectly accurate quantitative estimates of temperature-mediated responses with the kernels. Rather, we apply the kernels to assess whether model spread in dSWA/dT originates from differences in the response of atmospheric water vapour to surface warming. We argue that the kernel calculations are sufficiently accurate for this purpose. Extended Data Fig. 4 shows that kernel-derived dSWA/dT is significantly correlated across models with globally averaged responses of water vapour, as expected. By contrast, actual model-produced dSWA/dT is not correlated with the water vapour responses, supporting our finding that spread in dSWA/dT is not explained by differences in the water vapour response. This may be partly because water vapour responses (in a fractional sense) and their intermodel variability are largest in the upper troposphere, where water vapour changes have little effect on total-column SWA (Extended Data Fig. 4a). Our conclusions are also supported by ref. 12, where it is independently shown that CMIP5 spread in enhanced solar absorption under CO forcing cannot be reproduced by imposing simulated changes in atmospheric temperature and moisture on the single-column Fu-Liou radiative transfer model. Nonetheless, to assess uncertainty in kernel-estimated temperature-mediated responses, calculations with additional kernels produced from different radiation codes would be necessary. SWA sensitivity to water vapour variability is computed on the basis of spatial and temporal variations of PW in the control climate. All grid cells and months from 150 years of a model’s piControl simulation over the tropical oceans are aggregated into one sample to compute the sensitivity. The years 2001–2009 and 1984–2009 are used for estimates based on the CERES-EBAF (Clouds and the Earth’s Radiant Energy System Energy Balance and Filled) and ISCCP-FD (International Satellite Cloud Climatology Project flux data set) products, respectively. Tropical oceans are defined as grid cells with centres between 30° S and 30° N and with land fraction less than 0.50. All model output is regridded to 2.5° × 2.5° latitude–longitude before performing calculations. To compute the sensitivity, the clear-sky SWA at each grid cell and for each month is normalized by incoming solar radiation, then binned according to PW with equal bin size of 2 kg m−2. SWA is averaged within each PW bin and plotted against the bin centre value (Fig. 3a). Only bins with at least 20 data values in every model and observational source are considered, resulting in a common PW range of 12–58 kg m−2. The linear regression slope of the SWA versus PW scatterplot represents the SWA sensitivity to varying PW (that is, dSWA/dPW). Statistical uncertainty in dSWA/dPW is computed as the 95% confidence interval (CI) of the regression slope, derived from the estimated standard error of the slope parameter33. We consider only tropical oceans when computing dSWA/dPW owing to the relatively small variability of surface albedo and solar zenith angle within this region, better isolating the effect of PW on SWA. To examine whether the remaining small variations of surface albedo or solar zenith angle affect dSWA/dPW, we re-compute dSWA/dPW by also conditioning on surface albedo and/or zenith angle. Specifically, dSWA/dPW is recomputed from locations and months where albedo and/or zenith angle vary by no more than 0.01 or 1°, respectively. While the recomputed dSWA/dPW values vary slightly from the original values computed with the entire domain, they are highly correlated with the original values across models (not shown). This suggests that the influence of albedo and zenith angle on dSWA/dPW is very small. Our computation of dSWA/dPW does not isolate the effect of aerosols on SWA. Models differ in the types of aerosols represented, the concentrations and optical properties of the aerosols, and the quantitative parameterization of aerosol scattering and absorption. These factors could potentially modulate the relationship between SWA and PW in models, and thus dSWA/dPW may not completely reflect the physics of water vapour absorption. One possible example of this is with the GFDL models. All three GFDL models (GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M) use the same shortwave parameterization for water vapour absorption and scattering by aerosols34, 35, 36, 37; yet the CM3 absorbs more solar radiation in moist conditions, leading to larger dSWA/dPW (Extended Data Fig. 5). The CM3 implements an interactive aerosol scheme with different aerosol optical properties from the ESM models (which prescribe aerosols), resulting in enhanced and more realistic downward clear-sky surface shortwave flux due to reduced aerosol direct effects34. We speculate that the reduced aerosol direct effects may lead to more solar radiation available for absorption by lower tropospheric water vapour, and thus larger SWA in moist conditions in the CM3. The extent that differences in aerosol concentrations/properties affect the variability in dSWA/dPW among other models is not known without a more rigorous and controlled investigation. However, we suspect these effects are generally small, as (1) the GFDL models represent the only instance in which dSWA/dPW substantially differs among models with similar parameterizations of solar absorption by water vapour (Fig. 4, Extended Data Fig. 6), and (2) dSWA/dPW computed using different forcing scenarios (for example, historical and RCP8.5, which potentially exhibit large variability in aerosols) are very similar to those computed with the piControl (not shown). Stratospheric ozone is another strong absorber of solar radiation. Ozone may influence dSWA/dPW if its concentration varies systematically with atmospheric moisture. Although column-integrated ozone tends to decrease with increasing PW in 16 models that have available ozone output, there is no cross-model correlation between the sensitivity of ozone concentration to varying PW and dSWA/dPW (not shown). This suggests the covariability of ozone and PW does not systematically affect dSWA/dPW. Furthermore, dSWA/dPW is similar to that in Extended Data Fig. 5 when it is computed from a smaller sample of locations and months exhibiting little variability in column-integrated ozone (not shown). This strengthens the case that ozone is not significantly affecting model variability in dSWA/dPW. However, the parameterization of ozone absorption probably influences the mean position of the SWA versus PW curve for each model (that is, average SWA over the range of PW analysed). One possible example of this is with the INM-CM4 and MRI-CGCM3 models. Both models use the same shortwave parameterization for water vapour absorption38, but have different treatments of solar absorption by ozone39, 40. We speculate that this partly explains the similar slopes but different vertical placement of the SWA versus PW curves for these models (Extended Data Fig. 5). As discussed above, dSWA/dPW values are nearly invariant to the simulation (for example, piControl, abrupt4 × CO , historical, RCP8.5) from which they are calculated in models (not shown). They are also very similar when computed from a subset of years as short as that used from observations (2001–2009). Furthermore, dSWA/dPW computed from various percentiles of the SWA distribution within each PW bin (ranging from the 10th to 90th percentile) are similar to those computed with the SWA bin mean (not shown). These findings further demonstrate that the methodology robustly quantifies the dependence of SWA on atmospheric moisture. The CERES-EBAF data set provides clear-sky radiative fluxes at the TOA and surface on a grid comparable to climate models and has global coverage. TOA fluxes are based on satellite measurements, and surface fluxes are generated with a radiative transfer model and are constrained by the TOA fluxes22. Although the surface fluxes are model produced, they are computed with a radiative transfer algorithm that is arguably more advanced and physically based than that used in most climate models. In particular, the radiation code for solar absorption employs the formal correlated-k-distribution framework with absorption coefficients (k values) being determined directly from detailed line-by-line (LBL)-generated k distributions41, 42. This approach is arguably superior to that in most CMIP5 models, in which k values are in many cases determined with non-physical mathematical optimization procedures (for example, refs 36 and 43). In addition, the treatment of pressure–temperature–concentration dependence of k values in the CERES-EBAF scheme is more physical and higher in resolution than most CMIP5 models41. The final parameterization describing water vapour absorption in CERES-EBAF also has many mathematical terms (>50) approximating shortwave transmission44. CERES-EBAF surface fluxes are in good agreement with point observations22 and the radiation scheme used to generate surface fluxes performs well when compared with recent LBL calculations24. To compute CERES-EBAF-derived dSWA/dPW, water vapour data are taken from three sources: (1) the Special Sensor Microwave Imager (SSM/I)45; (2) a product developed by Remote Sensing Systems (RSS) that combines measurements from various instruments, including SSM/I, the Special Sensor Microwave Imager Sounder (SSMIS), the Advanced Microwave Scanning Radiometer (AMSR-E), and the WindSat Polarimetric Radiometer46; and (3) the Television Infrared Observation Satellite Operational Vertical Sounder (TOVS)47. ISCCP-FD-derived dSWA/dPW is based on TOVS water vapour, which was used in the development of the flux data set27. The uncertainty range of CERES-EBAF-derived dSWA/dPW shown in the figures in this Letter (for example, Figs 3 and 4) reflects statistical uncertainty in the computation of dSWA/dPW and uncertainty due to the use of different PW data sets (see Fig. 3 legend). Inherent uncertainties in CERES-EBAF fluxes, including possible measurement uncertainty, errors in the radiative transfer scheme, and uncertainty in the methodology that generates clear-sky fluxes22, are difficult to quantify and are not included. Potential uncertainty in the individual PW measurements are also not accounted for. Thus, the effective uncertainty of observed dSWA/dPW is probably larger than indicated in the figures. Nonetheless, the conclusion that most CMIP5 models underestimate dSWA/dPW is robust, as it is strongly supported by a recent study in which radiation schemes are evaluated against high quality LBL calculations25. The severe underestimation of dSWA/dPW by the GISS models stands out in Fig. 4. In these models, solar absorption by water vapour is parameterized with a pseudo-k-distribution approach consisting of 15 mathematical terms48, 49, 50, 51, 52. Some of the other CMIP5 models in our analysis, including those with the largest dSWA/dPW, use as many terms in their parameterizations (Fig. 4). Thus the poor performance of the GISS parameterization is not simply the result of the number of computations employed to approximate shortwave transmission. Rather, the finer details of how the parameters of the analytical expressions (for example, pseudo absorption coefficients and weights for terms) are developed and the quality of the reference calculations from which the parameterizations were originally based, are probably important, among other characteristics. The GISS parameterization employed in CMIP5 was developed from a combination of old and relatively new methods50, 51, 52. The resulting analytical expressions, which combine pressure–temperature–spectral absorption dependency, are known to underestimate solar absorption in moist atmospheres based on comparison with modern LBL calculations24, 25. Updates were made to the GISS radiation scheme since CMIP5, and these will probably result in significant improvements in solar absorption and dSWA/dPW in future generations of the GISS model25. Apart from the GISS models, the number of mathematical terms employed in shortwave parameterizations appear to exert a general influence on parameterization performance (Fig. 4). A specific example is with models developed at IPSL and CNRM. In these models, water vapour absorption is parameterized with an algorithm originally developed in 1980 and later modified for use in the operational European Center for Medium Range Weather Forecasts (ECMWF) model53, 54. It consists of a few shortwave bands, within which Padé Approximants represent gaseous absorption by water vapour53. In the IPSL models, only two shortwave bands are used54. These models clearly underestimate mean SWA over the range of PW analysed and dSWA/dPW is smaller than that of all models except GISS (Extended Data Fig. 5). By contrast, the CNRM scheme employs a total of 6 shortwave bands28. Mean SWA is considerably larger and more realistic in these models compared to IPSL and dSWA/dPW is marginally improved as well (Extended Data Fig. 5). Thus the number of spectral bands and corresponding computations can have a large impact on the realism of solar absorption. A comparison of the models that implement a 7-band parameterization originally developed in ref. 38 (BCC-CSM1.1, BCC-CSM1.1(m), CCSM4, INM-CM4, MRI-CGCM3, NorESM1-M) sheds light on specific characteristics of parameterizations of gaseous absorption, other than number of mathematical terms, that appear important for SWA. The original parameterization for water vapour absorption in ref. 38 consists of a 7-term pseudo-k-distribution summation with absorption coefficients and weights determined by fits to empirical and LBL calculations, respectively38, 50. It is employed by the INM-CM4 and MRI-CGCM3 models (Extended Data Fig. 6). The parameterization was later modified to account for additional near-infrared water vapour absorption based on updated spectroscopic data and continuum absorption in the shortwave, which resulted in refitting the 7 parameterized absorption coefficients55. The updated parameterization is employed in the BCC-CSM1.1, BCC-CSM1.1(m), CCSM4, and NorESM1-M models. The models using the updated parameterization exhibit improved dSWA/dPW by a small but non-negligible amount (Extended Data Fig. 6). Consideration of weak water vapour absorption lines and continuum absorption are therefore somewhat important for accurate simulation of changes in solar absorption in a warming climate. This is consistent with the findings presented in refs 55 and 56. Note that most models with larger and more realistic dSWA/dPW tend to account for continuum absorption in their parameterizations (Extended Data Fig. 6). Other details of the CMIP5 shortwave parameterization schemes, including the treatment of scattering by aerosols and molecules and of overlapping absorption by multiple gaseous species, have not been thoroughly investigated here. They too may influence the intermodel spread in dSWA/dPW. Even if these details are indeed influencing dSWA/dPW, it would not change the conclusion that shortwave parameterizations in general are important for the spread in simulated hydrologic cycle intensification. We exploit the strong model relationships among dSWA/dPW, dSWA/dT, and L dP/dT (Extended Data Fig. 7a, b) to compute a hypothetical change in L P that may occur at the end of the twenty-first century under realistic climate forcing if the true temperature-mediated SWA response to CO forcing, dSWA/dT, were perfectly known. The ‘true’ dSWA/dT is approximated from the model relationship between dSWA/dT and dSWA/dPW, using the observed value of dSWA/dPW based on CERES-EBAF (Extended Data Fig. 7a). The resulting true value of dSWA/dT is then used with the model relationship between the temperature-mediated L P response, L dP/dT, and dSWA/dT to estimate a ‘bias’ in L dP/dT that originates from a bias in dSWA/dT (Extended Data Fig. 7b). The bias for each model is then removed from the predicted precipitation change at the end of the twenty-first century in the RCP8.5 scenario relative to the piControl according to: where is the constrained total change in L P normalized by surface warming with the bias removed, L ΔP is the total late twenty-first century L P change (mean of years 2081–2100 in RCP8.5 minus mean of years 131–150 in piControl), and ΔT is the late twenty-first century 2-m air temperature change computed similarly to L ΔP . The above procedure makes several assumptions, including: (1) the middle of the range in CERES-EBAF-computed dSWA/dPW is most representative of the real atmosphere; (2) the best ‘true’ value of dSWA/dT (Extended Data Fig. 7a, blue star) and L dP/dT (Extended Data Fig. 7b, black horizontal line) occurs at the linear regression line on the cross-model scatterplots; and (3) L dP/dT contributes linearly (with ΔΤ) to the total late twenty-first century change in L P computed from RCP8.5, as depicted in equation (2). Removing the bias in L dP/dT due to a bias in dSWA/dT reduces the model spread in predicted precipitation change per unit warming at the end of the twenty-first century by 37%, and reduces the ensemble mean increase by 38% (Extended Data Fig. 7c). Even if we do not normalize by differences in surface warming ΔT, which is the main driver of the spread in total precipitation change L ΔP, a discernible reduction in spread by 27% and ensemble mean increase by 25% can be achieved (Extended Data Fig. 7d). The spread reduction is substantial, considering the numerous factors in addition to the temperature-mediated SWA response to CO forcing potentially contributing to model scatter in RCP8.5 projections. These include temperature-mediated responses of other energy budget components (Extended Data Fig. 2), greenhouse-gas forcing other than CO , aerosols, and the rapid L P adjustments to all forcings. For instance, ref. 4 demonstrated the potent role of black carbon forcing for CMIP3 spread in simulated global precipitation change under a realistic climate change scenario. That we obtain a 37% reduction in L ΔP/ΔT under RCP8.5 by only constraining the temperature-mediated component of SWA change under pure CO forcing (and only a somewhat larger reduction by 45% when repeating the same exercise with the quadrupled CO runs, not shown) suggests that the role of black carbon forcing on the spread may be less potent in CMIP5 than CMIP3. This is an interesting possibility worthy of further analysis. How may uncertainty in late twenty-first century precipitation change be reduced further? As discussed above, a realistic climate change scenario includes greenhouse-gas forcing (from CO and other gases), aerosol forcing, and rapid adjustments to these forcings. A better understanding of all these factors is therefore critical, including understanding of the rapid adjustment to CO forcing. This factor has a non-negligible spread (Extended Data Fig. 3b) and is not strongly correlated with the corresponding intermodel variations in temperature-mediated response (r = −0.23, not shown). Additionally, the spread in the temperature-mediated L P response to CO forcing is not only driven by the SWA component, as indicated by residual scatter in Fig. 2b. Extended Data Fig. 2 shows that the net atmospheric longwave cooling response, dLWC/dT, also has a large spread that is correlated with L dP/dT. dSWA/dT and dLWC/dT are not correlated with each other (|r| < 0.1, not shown), suggesting that dLWC/dT is another independent source of spread that demands better understanding. dLWC/dT is only correlated with L dP/dT for all-sky (Extended Data Fig. 2), implying that clouds may play an important role in the intermodel relationship. This is different from the case of dSWA/dT, in which clear-sky absorption by water vapour is critical. No statistical methods were used to predetermine sample size. Any codes used in the analysis in this paper and in the production of figures can be made available upon request. Please contact A.M.D. (firstname.lastname@example.org).
El Amraoui L.,Meteo - France |
Attie J.-L.,Meteo - France |
Attie J.-L.,CNRS Laboratory for Aerology |
Semane N.,CNRM |
And 11 more authors.
Atmospheric Chemistry and Physics | Year: 2010
This paper presents a comprehensive characterization of a very deep stratospheric intrusion which occurred over the British Isles on 15 August 2007. The signature of this event is diagnosed using ozonesonde measurements over Lerwick, UK (60.14° N, 1.19° W) and is also well characterized using meteorological analyses from the global operational weather prediction model of Météo- France, ARPEGE. Modelled as well as assimilated fields of both ozone (O 3) and carbon monoxide (CO) have been used in order to better document this event. O 3 and CO from Aura/MLS and Terra/MOPITT instruments, respectively, are assimilated into the three-dimensional chemical transport model MOCAGE of Météo-France using a variational 3-DFGAT (First Guess at Appropriate Time) method. The validation of O 3 and CO assimilated fields is done using selfconsistency diagnostics and by comparison with independent observations such as MOZAIC (O 3 and CO), AIRS (CO) and OMI (O 3). It particularly shows in the upper troposphere and lower stratosphere region that the assimilated fields are closer to MOZAIC than the free model run. The O 3 bias between MOZAIC and the analyses is ?11.5 ppbv with a RMS of 22.4 ppbv and a correlation coefficient of 0.93, whereas between MOZAIC and the free model run, the corresponding values are 33 ppbv, 38.5 ppbv and 0.83, respectively. In the same way, for CO, the bias, RMS and correlation coefficient between MOZAIC and the analyses are ?3.16 ppbv, 13 ppbv and 0.79, respectively, whereas between MOZAIC and the free model they are 6.3 ppbv, 16.6 ppbv and 0.71, respectively. The paper also presents a demonstration of the capability of O 3 and CO assimilated fields to better describe a stratosphere-troposphere exchange (STE) event in comparison with the free run modelled O 3 and CO fields. Although the assimilation of MLS data improves the distribution of O 3 above the tropopause compared to the free model run, it is not sufficient to reproduce the STE event well. Assimilated MOPITT CO allows a better qualitative description of the stratospheric intrusion event. The MOPITT CO analyses appear more promising than the MLS O3 analyses in terms of their ability to capture a deep STE event. Therefore, the results of this study open the perspectives for using MOPITT CO in the STE studies. © 2010 Author(s).
Baumgardner D.,National Autonomous University of Mexico |
Brenguier J.L.,CNRM |
Bucholtz A.,U.S. Navy |
Coe H.,University of Manchester |
And 14 more authors.
Atmospheric Research | Year: 2011
An overview is presented of airborne systems for in situ measurements of aerosol particles, clouds and radiation that are currently in use on research aircraft around the world. Description of the technology is at a level sufficient for introducing the basic principles of operation and an extensive list of references for further reading is given. A number of newer instruments that implement emerging technology are described and the review concludes with a description of some of the most important measurement challenges that remain. This overview is a synthesis of material from a reference book that is currently in preparation and that will be published in 2012 by Wiley. © 2011 Elsevier B.V.
Hamdi R.,Royal Meteorological Institute of Belgium |
Degrauwe D.,Royal Meteorological Institute of Belgium |
Duerinckx A.,Royal Meteorological Institute of Belgium |
Duerinckx A.,Ghent University |
And 15 more authors.
Geoscientific Model Development | Year: 2014
The newly developed land surface scheme SURFEX (SURFace EXternalisée) is implemented into a limited-area numerical weather prediction model running operationally in a number of countries of the ALADIN and HIRLAM consortia. The primary question addressed is the ability of SURFEX to be used as a new land surface scheme and thus assessing its potential use in an operational configuration instead of the original ISBA (Interactions between Soil, Biosphere, and Atmosphere) scheme. The results show that the introduction of SURFEX either shows improvement for or has a neutral impact on the 2 m temperature, 2 m relative humidity and 10 m wind. However, it seems that SURFEX has a tendency to produce higher maximum temperatures at high-elevation stations during winter daytime, which degrades the 2 m temperature scores. In addition, surface radiative and energy fluxes improve compared to observations from the Cabauw tower. The results also show that promising improvements with a demonstrated positive impact on the forecast performance are achieved by introducing the town energy balance (TEB) scheme. It was found that the use of SURFEX has a neutral impact on the precipitation scores. However, the implementation of TEB within SURFEX for a high-resolution run tends to cause rainfall to be locally concentrated, and the total accumulated precipitation obviously decreases during the summer. One of the novel features developed in SURFEX is the availability of a more advanced surface data assimilation using the extended Kalman filter. The results over Belgium show that the forecast scores are similar between the extended Kalman filter and the classical optimal interpolation scheme. Finally, concerning the vertical scores, the introduction of SURFEX either shows improvement for or has a neutral impact in the free atmosphere.
Llovel W.,LEGOS OMP |
Becker M.,LEGOS OMP |
Cazenave A.,LEGOS OMP |
Jevrejeva S.,Proudman Oceanographic Laboratory |
And 5 more authors.
Global and Planetary Change | Year: 2011
On decadal to multidecadal time scales, thermal expansion of sea waters and land ice loss are the main contributors to sea level variations. However, modification of the terrestrial water cycle due to climate variability and direct anthropogenic forcing may also affect sea level. For the past decades, variations in land water storage and corresponding effects on sea level cannot be directly estimated from observations because these are almost unexistent at global continental scale. However, global hydrological models developed for atmospheric and climatic studies can be used for estimating total water storage. For the recent years (since mid-2002), terrestrial water storage change can be directly estimated from observations of the GRACE space gravimetry mission. In this study, we analyse the interannual variability of total land water storage, and investigate its contribution to mean sea level variability at interannual time scale. We consider three different periods that, each, depend on data availability: (1) GRACE era (2003-2009), (2) 1993-2003 and (3) 1955-1995. For the GRACE era (period 1), change in land water storage is estimated using different GRACE products over the 33 largest river basins worldwide. For periods 2 and 3, we use outputs from the ISBA-TRIP (Interactions between Soil, Biosphere, and Atmosphere-Total Runoff Integrating Pathways) global hydrological model. For each time span, we compare change in land water storage (expressed in sea level equivalent) to observed mean sea level, either from satellite altimetry (periods 1 and 2) or tide gauge records (period 3). For each data set and each time span, a trend has been removed as we focus on the interannual variability. We show that whatever the period considered, interannual variability of the mean sea level is essentially explained by interannual fluctuations in land water storage, with the largest contributions arising from tropical river basins. © 2010 Elsevier B.V.
Najac J.,European Center for Research and Advanced Training in Scientific Computation |
Lac C.,CNRM |
Terray L.,European Center for Research and Advanced Training in Scientific Computation
International Journal of Climatology | Year: 2011
A statistical-dynamical downscaling method is presented to estimate 10 m wind speed and direction distributions at high spatial resolutions using a weather type based approach combined with a mesoscale model. Daily 850 hPa wind fields (predictors) from ERA40 reanalysis and daily 10 m wind speeds and directions (predictands) measured at 78 meteorological stations over France are used to build and validate the downscaling algorithm over the period 1974-2002. First of all, the daily 850 hPa wind fields are classified into a large number of wind classes and one day is randomly chosen inside each wind class. Simulations with a non-hydrostatic mesoscale atmospheric model are then performed for the selected days over three interactively nested domains over France, with finest horizontal mesh size of 3 km over the Mediterranean area. The initial and coupling fields are derived from the ERA40 reanalysis. Finally, the 10 m wind distributions are reconstructed by weighting each simulation by the corresponding wind class frequency. Evaluation and uncertainty assessment of each step of the procedure is performed. This method is then applied for a climate change impact study: daily 850 hPa wind fields from 14 general circulation models of the CMIP3 multimodel dataset are used to determine evolutions in the frequency of occurrence of the wind classes and to assess the potential evolution of the wind resources in France. Two time periods are focused on: a historical period (1971-2000) from the climate of the twentieth century experiment and a future period (2046-2065) from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) experiment. Evolution of the 10 m winds in France and associated uncertainties are discussed. Significant changes are depicted, in particular a decrease of the wind speed in the Mediterranean area. Copyright © 2010 Royal Meteorological Society.
Swingedouw D.,European Center for Research and Advanced Training in Scientific Computation |
Swingedouw D.,CEA Saclay Nuclear Research Center |
Terray L.,European Center for Research and Advanced Training in Scientific Computation |
Cassou C.,European Center for Research and Advanced Training in Scientific Computation |
And 3 more authors.
Climate Dynamics | Year: 2011
The variability of the climate during the last millennium is partly forced by changes in total solar irradiance (TSI). Nevertheless, the amplitude of these TSI changes is very small so that recent reconstruction data suggest that low frequency variations in the North Atlantic Oscillation (NAO) and in the thermohaline circulation may have amplified, in the North Atlantic sector and mostly in winter, the radiative changes due to TSI variations. In this study we use a state-of-the-art climate model to simulate the last millennium. We find that modelled variations of surface temperature in the Northern Hemisphere are coherent with existing reconstructions. Moreover, in the model, the low frequency variability of this mean hemispheric temperature is found to be correlated at 0.74 with the solar forcing for the period 1001-1860. Then, we focus on the regional climatic fingerprint of solar forcing in winter and find a significant relationship between the low frequency TSI forcing and the NAO with a time lag of more than 40 years for the response of the NAO. Such a lag is larger than the around 20-year lag suggested in other studies. We argue that this lag is due, in the model, to a northward shift of the tropical atmospheric convection in the Pacific Ocean, which is maximum more than four decades after the solar forcing increase. This shift then forces a positive NAO through an atmospheric wave connection related to the jet-stream wave guide. The shift of the tropical convection is due to the persistence of anomalous warm SST forcing the anomalous precipitation, associated with the advection of warm SST by the North Pacific subtropical gyre in a few decades. Finally, we analyse the response of the Atlantic meridional overturning circulation to solar forcing and find that the former is weakened when the latter increases. Changes in wind stress, notably due to the NAO, modify the barotropic streamfunction in the Atlantic 50 years after solar variations. This implies a wind-driven modification of the oceanic circulation in the Atlantic sector in response to changes in solar forcing, in addition to the variations of the thermohaline circulation. © 2010 Springer-Verlag.
Degrauwe D.,RMI Belgium |
Seity Y.,CNRM |
Bouyssel F.,CNRM |
Termonia P.,RMI Belgium |
Termonia P.,Ghent University
Geoscientific Model Development | Year: 2016
General yet compact equations are presented to express the thermodynamic impact of physical parameterizations in a NWP or climate model. By expressing the equations in a flux-conservative formulation, the conservation of mass and energy by the physics parameterizations is a builtin feature of the system. Moreover, the centralization of all thermodynamic calculations guarantees a consistent thermodynamical treatment of the different processes. The generality of this physics-dynamics interface is illustrated by applying it in the AROME NWP model. The physics-dynamics interface of this model currently makes some approximations, which typically consist of neglecting some terms in the total energy budget, such as the transport of heat by falling precipitation, or the effect of diffusive moisture transport. Although these terms are usually quite small, omitting them from the energy budget breaks the constraint of energy conservation. The presented set of equations provides the opportunity to get rid of these approximations, in order to arrive at a consistent and energy-conservative model. A verification in an operational setting shows that the impact on monthlyaveraged, domain-wide meteorological scores is quite neutral. However, under specific circumstances, the supposedly small terms may turn out not to be entirely negligible. A detailed study of a case with heavy precipitation shows that the heat transport by precipitation contributes to the formation of a region of relatively cold air near the surface, the so-called cold pool. Given the importance of this cold pool mechanism in the life cycle of convective events, it is advisable not to neglect phenomena that may enhance it. © 2016 Author(s).
Termonia P.,Royal Meteorological Institute of Belgium |
Termonia P.,Ghent University |
Voitus F.,CNRM |
Degrauwe D.,Royal Meteorological Institute of Belgium |
And 2 more authors.
Monthly Weather Review | Year: 2012
This paper describes the implementation of a proposal of Boyd for the periodization and relaxation of the fields in a full three-dimensional spectral semi-implicit semi-Lagrangian limited-area model structure of an atmospheric modeling system calledHARMONIE that is used for numerical weather prediction and regional climate studies. Some first feasibility tests in an operational numerical weather prediction context are presented. They show that, in terms of standard operational forecast scores, Boyd's windowing-based method provides comparable performance as the old existing spline-based periodization procedure. However, the real improvements of this method should be expected in specific cases of strong dynamical forcings at the lateral boundaries. An extensive demonstration of the superiority of this windowing-based method is provided in an accompanying paper. ©2012 American Meteorological Society.
Woppelmann G.,University of La Rochelle |
Marcos M.,CSIC - Mediterranean Institute for Advanced Studies |
Santamaria-Gomez A.,University of La Rochelle |
Martin-Miguez B.,CETMAR |
And 2 more authors.
Geophysical Research Letters | Year: 2014
Tide gauge records are the primary source of sea level information over multidecadal to century timescales. A critical issue in using this type of data to determine global climate-related contributions to sea level change concerns the vertical motion of the land upon which the gauges are grounded. Here we use observations from the Global Positioning System for the correction of this vertical land motion. As a result, the spatial coherence in the rates of sea level change during the twentieth century is highlighted at the local and the regional scales, ultimately revealing a clearly distinct behavior between the Northern and the Southern Hemispheres with values of 2.0 mm/yr and 1.1 mm/yr, respectively. Our findings challenge the widely accepted value of global sea level rise for the twentieth century. Key Points Detection of a spatial pattern between hemispheres in secular sea level rates Use of most advanced methods and data for studying secular trends in sea level Vertical land motion: An obstacle to detecting fingerprints in sea level change ©2014. American Geophysical Union. All Rights Reserved.