Utilizing Increments in Model Development

MERRA data has complete budgets for water and energy, including the incremental analysis updates (IAU) that constrain the model forecast to the analyzed observations. The increments can be interpreted instantaneously (at the six hour analysis) as a representation of the forecast error, or for longer terms as the mean model bias. The magnitude of the IAU terms are not trivial, and should be accounted for, certainly in budget studies, but can also be useful in understanding the representation of weather and climate phenomena in reanalyses.

As an example of utilizing the increments to evaluate the background model, Mapes and Bacmeister (2012) have evaluated MERRA's tropical climate and convection, relating significant IAU values to weaknesses in the representation of physical processes. They suggest diagnostics and potential areas for model development.

Aircraft Temperatures

Early in the MERRA reanalysis period, aircraft observations are sparse, but increase in time, eventually providing a significant amount of conventional observations. Cardinali et al. (2003) identified biases in aircraft temperature observations, and Ballish and Kumar (2008) further examined the biases in each type of commercial aircraft. Figure 1 shows an 2001-2009 mean bias between collocated aircraft and radiosonde 200mb temperature, over the United  States. Almost everywhere, aircraft are warmer than the radiosonde observations.

Figure 1 Collocated aircraft/RAOB at 200mb temperature (K) differences assimilated in MERRA  averaged from 2001-2012. (computed from the differences of each observaitons background departure)

Figure 2 shows the monthly mean difference between collocated radiosonde and aircraft observations assimilated in MERRA over the U. S., while the dots show the number of collocations (thou/yr).  While the data for these figures are binned and gridded, area and monthly averaging include weighting for the number of observations. Early in the reanalysis, there are lower numbers of aircraft observations, and the differences reflect that with more monthly variability. In 1990-1991, increasing number of observations increase the distribution of data, and the warm bias converges. After 1996, there is an exaggeration in the annual cycle, where the summer aircraft observations get even warmer. However, every month is a positive difference.

Figure 2 Time series of monthly mean differences of 200mb temperature collocations over the United States (Aircraft minus RAOB OmF, in red, K). The black dots indicate number of collocations each year (in thousands, right axis).

The increasing number of collocations reflects the increase in availability of aircraft observations. There are many more aircraft observations being assimilated away from the vicinity of the radiosondes. The number of observations then influences the data assimilation, where the analysis is drawn toward the aircraft data. Figure 3 shows the time series of background departure for collocated radiosonde and aircraft 200mb temperature. As the aircraft observations increase in number, their background departure decreases (this also holds for the RMS of the background departure).

Figure 3 Time series of monthly mean background departure (OmF) of the collocated RAOB (black) and Aircraft (red) 200mb temperatures (K, left axis). The black dots indicate number of collocations each year (in thousands, right axis).

Both Cardinali et al (2003) and Ballish and Kumar (2008) have suggested bias corrections for commercial aircraft temperature data, using more limited comparisons than these. ECMWF has implemented a bias correction in their forecast system.

Reanalyses trends

One of the most important topics and calculations in climate science is trend, aiming to determine long term changes. Significant issues exist in the observational record, and methods correcting the problems themselves need to be explained and verified. In a recent update to the U.S. Historical Climate Network (HCN) station data, Vose et al. compare the observational record against several reanalyses near surface air temperature and their ensemble. In looking at the continental United States, their Figure 1 shows the revised HCN trend is larger than the uncorrected data, but also remarkably close to the ensemble of the reanalyses. Also, despite the differences in trends of the reanalyses, there is very good agreement in the reanalyses interannual variability around the trends (their figure 3). The bottom line is that the corrections to HCN are in agreement with reanalyses (all are statistically significant warm trends), but it is noted that this is not a validation of the corrections.

The spatial distribution of reanalysis trend relative to the HCN trend shows substantial local variations among the reanalyses (their figure 4). So, while the observational forcing imposed on reanalyses can influence the large scale features, the model predictions used to make the analyses impart  some uncertainty related to the model physical parameterizations. If the model errors are random, the ensemble should then minimize the error. Errors that are systematic among all reanalyses would persist in the ensemble.

This paper demonstrates some important points about reanalyses. Any one reanalysis may have uncertainty in any given research project. Multiple reanalyses can help identify these uncertainties and perhaps the background model biases in the reanalysis. However, the reanalyses output variables being compared must have equivalent formulations to take advantage of the availability of the current modern reanalyses through such intercomparisons. Likewise, on hourly surface output in MERRA and CFSR were useful in this study.

MERRA Special Collection

Papers are now available at the AMS Online Journals MERRA Special Collection. The MERRA Overview by Rienecker et al. should be considered as the fundamental citation for the MERRA project and data set.

Publications Page growing

With the holidays, travel and deadlines it has been difficult to put together regular snippets of interesting results. The hope was to summarize the papers coming out but there are quite a few and a lot of useful information. I hope to get back to that in the coming weeks.

In the mean time, it is important to share the information in a timely fashion, and the speed that the internet and electronic publishing permits is much greater now than anytime before. The GMAO is collecting information and manuscripts with permission of the authors on our www site. The MERRA Publications page is growing well, most manuscripts are presently submitted for publication and some already accepted. In addition, while most of the papers are written by GMAO staff, quite a few have been authored outside the GMAO, without a GMAO co-author. Most papers have some critical review of the realism of the system. It is important for the development of the systems to account for the strengths and weaknesses, and is a challenge to improve the system while keeping the processes that are already well represented. Shared knowledge would be critical to this development.

Included on the page is the general overview description of the project: Rienecker, M.M., et al., 2011. MERRA - NASA's Modern-Era Retrospective Analysis for Research and Applications. J. Climate (submitted). Check the page linked above for the latest status on the paper. At this time it is still being reviewed.

New Results: High Latitude Fluxes

There are a number of presentations that include MERRA data at the Workshop: Surface Fluxes: Challenges for High Latitudes in Boulder CO this week. Given the scarcity of observations at high latitudes and uncertainty in satellite observations, reanalyses may be useful data sets.

Michael Brunke, Xubin Zeng,( Uncertainties in global surface flux datasets in high latitudes.) compare various reanalyses and remote sensing products with tower and ship data from various field experiments (CATCH, FASTEX and SHEBA). The results show MERRA is much more comparable to the station data than the previous generations reanalyses, and in range of the remotely sensed products (e.g. Figure 1). The poster is available online.


Figure 1 Comparison of 6-hourly mean wind stress from MERRA, ERA-40, NCEP-R1, and NCEP-R2. The solid lines are the one-to-one slope and the dashed lines are the straight-line regressions with the regression slopes indicated.

Richard Cullather. Evaluation of Arctic Energy and Moisture Budgets in the MERRA Reanalysis. has also compared MERRA Arctic data with long term station measurements and some field experiment sites. For example, Figure 2 shows the time series of MERRA precipitation compared to observations made at a drift camp site. While there are some discrepancies in the magnitude of a few events, the comparison (and correlations of 0.74) seems quite reasonable given the uncertainties at high latitudes.

Figure 2 Drift Camp observed precipitation compared to MERRA. The points are 7 day running means over the period late 1987 through 1990 (a total of 1200 days).

In addition, there is:
J. Brent Roberts, Franklin R. Robertson, Carol Anne Clayson, Analysis of atmosphere-ocean surface flux feedbacks in recent satellite and model reanalysis products.

More on that later.

Full presentations will be posted online at the workshop site shortly after the conclusion.

Water vapor feedback

An early decision in producing MERRA was to release data to the science community before the completion of the full time series, in the hopes that early analysis would provide insight to the data in a timely manner. In this months Journal of Climate, Dessler and Wong evaluate the water vapor feedback in the climate system using AR5 models, ERA40 and MERRA. The models and reanalyses all show consistent positive feedback (see the excerpt figure below). MERRA (point L in the figure) does show a bit more variability than the climate models (points A-J). A contributing factor to that variability may be a smaller number of years considered for MERRA, as the data was analyzed early in the production. Even so, the inclusion of MERRA in this research does help characterize the system and contribute to the understanding of its capabilities for climate research. At present, 1979 through Feb 2008 are approved for release at the MDISC data site.