Regional Climate Variaiblity and Historical Extreme Events

Partly following on to the initial evaluation of the 1993 Midwest Flooding, and also in working toward evaluation of MERRA and reanalyses for the National Climate Assessment, we have looked closer at US regional climate variability in reanalyses. While the Northwestern US summer precipitation  is handled quite well in all reanalyses (specifically NCEP CFSR and ERA Interim), owing to influence from ENSO teleconnections, the Midwestern region summer precipitation has substantial uncertainty across all reanalyses. In MERRA, for example, the interannual variance is noticeably low, so that droughts are not as dry and pluvial periods not as wet (see 1988 and 1993 respectively in the following figure).

The extreme summers of 1988 and 1993 have been tied to both large scale ENSO teleconnections and local land-atmosphere feedback processes. Given that the reanalyses data assimilation provides a strong reference for the large scale meteorology, the land atmosphere interactions would be a likely weak point in the models that may affect this uncertainty.

These results are discussed in further detail http://dx.doi.org/10.1175/JAMC-D-12-0291.1.

Extreme Precipitation

Some time ago, I saw a poster that showed observed extreme precipitation increasing in time along the east coast and Gulf coast of the US, suggesting increasing extremes due to land falling hurricanes (Ashouri et al, 2012). There is also some supporting analysis of increasing precipitation trends and extremes in the recent National Climate Assessment report (Figures 2.15 and 2.16). To narrow the results to potential hurricane sources, Figure 1 here evaluates the trend of maximum daily precipitation, each season from 1979-2012, where hurricane season is defined as June through November.  Significant trends are seen along the northeast US track as well as some trends along the Gulf coast track in the south east US.

Figure 1 Trend of maximum daily precipitation in each hurricane season from 1979-2012. Trends significantly different from zero at 90% confidence are outlined in white contours.

The MERRA reanalysis is able to reproduce, generally this pattern of increasing extreme precipitation (Figure 2). MERRA's increasing trends in the southeast have a wider area, and in the northeast, the strongest trends  do not extend through the New England states, as observed. Still the reproduction of the trends of such a specialized diagnostic in a relatively coarse reanalysis is noteworthy.


Figure 2 As in Figure 1, except for the precipitation produced in the MERRA reanalysis.

As a further test of these trends, we area average the observed hurricane season maximum precipitation for the North Atlantic states in MERRA and the CPC observations. The interannual variability of the extreme precipitation is well reproduced, though, MERRA's mean value tends to be less than observed. Figure 3 shows increases in time for the northeast, and not just some end point variation caused be recent very large storms (e.g. Irene). though, low anomalies can occur in the recent few years, as well.

Remnants of Tropical Storm Karen produced heavy precipitation over a substantial portion of the Northeast, so that the 2013 season in the northeast will likely also be a positive anomaly (here is some result of that storm). The southeast may not have comparable extreme precipitation in 2013, at least related to tropical storms and hurricanes. We will come back to this as the 2013 hurricane season closes and MERRA is extended through it.


Figure 3 Time series of area averaged extreme precipitation anomalies from CPC gauge observations and MERRA reanalysis. The mean value removed for comparing anomalies is presented in the legend.

Extrapolation to P > Ps

As many have found, MERRA pressure level data does not provide values for pressure surfaces when they are greater than the surface pressure (e.g. high topography).  Other reanalyses extrapolate the data using the surface meteorology and assumed lapse rates. This data may be useful in some cases such as zonal averaging, stream functions and thickness calculations.

A recent post at reanalysis.org provides user developed codes to fill these undefined grid points. This should be useful as one could adapt the codes to the filling method applied in other reanalyses to better match their extrapolated data.

See Extrapolation of MERRA Reanalyses to obtain continuous fields for more information.

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.

April 2011 Precipitation Extremes

During last April, record or near-record precipitation occurred across a large section of the United States (from the Midwest through the Ohio Valley). This rain, and likely snow melt from the Northern Great Plains, are contributing to the current prolonged and severe flooding along the Mississippi River and its delta. In addition, a prolonged drought of varying degrees persists across the Gulf Coast states.

MERRA precipitation (color shaded) with CPC gauge observations (black contour) time averaged for April 2011. (units: mm/day)

This week, MERRA data for April 2011 was released at the MDISC, roughly two weeks behind real time. Preliminary comparison with the CPC gauge data shows that MERRA precipitation is generally weaker than observed especially in southern Missouri, though the maximum in Pennsylvania is an overestimate. Because the reanalysis system is strongly constrained by observations, the weather systems that produce the rain, and hence the occurrence of rain events, are faithfully reproduced. The physical process of producing the precipitating water then leads to the error in the data product (assuming that the rain gauges capture the extent of the precipitating mass). It is worthwhile to note that MERRA does not assimilate precipitation observations over land, as in NARR.

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.

Latest Data being reprocessed

Earlier this year, a compiler was upgraded on our supercomputing platform. This upgrade has apparently introduced a problem into the post processing program that compresses and prepares data files for transfer to the DISC where the data are publicly accessible. So that: all MERRA data after data date June 1, 2010 will be replaced and is currently no longer accessible at the DISC. This includes Monthly means from May 2010 on, but not the May 2010 data.

For those that may have downloaded data for the period June 1 - August 31, 2010, consider very carefully whether to continue using the data. So far, the only variables we have confirmed are corrupted are the roughness lengths for momentum and heat. We have no reason to think that other variables are not affected, hence the recall. The GMAO does not recommend the use of this data if you downloaded it before this recall.

The MERRA system and archive data are not affected by this issue. The system continues to run in near real time. Once the post processing code is fixed, the archive data will be reprocessed and posted to the DISC.

Late Oct 29 Update: The code has been fixed and tested. Corrected files are being reprocessed and sent to the DISC.

MERRA Climate Atlas

The MERRA Climate Atlas http://gmao.gsfc.nasa.gov/ref/merra/atlas/ has been opened and linked from the homepage. Numerous figures compare MERRA with previous and current reanalyses as well as global observation data when available. As is mentioned in the Introduction, new figures and comparisons are still planned. Comments and suggestions on the Atlas content are welcome.

Pressure levels greater than surface pressure

Some questions have come in regarding differences between MERRA and other reanalysis at pressure 1000mb and 850mb pressure levels. It is very important to note that MERRA does not extrapolate pressure level data vertically greater than the surface pressure. The result is that there is undefined data points in much of the 1000mb fields. This will affect the representativeness of both time and area averages of MERRA data compared with reanalyses or other data that extrapolates gridpoints to pressure levels greater than the surface pressure. The GMAO provides a summary of the impact that this has on averaging. In addition, the FAQ will be updated to call out this difference with other data sets. The main GMAO www page also has other pages with useful practical information about the data and assimilation system.

Katrina Quick Look

In looking at some land hydrology in the southern US, a question came up on the effect of the 2005 hurricane season on the hydrology time series. So, we started looking around at the evolution of Katrina. This animation shows the MERRA version of Katrina (13Mb gif) moving over the southern tip of Florida and through the Gulf of Mexico. The colors show precipitation in mm/day and the white contours are sea level pressure (contour interval 2mb). The Best Track location is plotted every six hours. Firstly, the MERRA closed low pressure follows the best track fairly well throughout the evolution. This is notable only in that while MERRA does assimilate observations every six hours, there is no relocating or bogusing routines involved with the analysis/forecast cycles.

The MERRA resolution (1/2 degree) is not fine enough to get at the mesoscale structures in hurricanes, and we see that in MERRA where the surface winds (not shown) only reach Category 2 on the Saffir-Simpson scale (observations and estimates of Category 5 occurred during Katrina). Likewise, the rainbands at a distance from the central low are not well defined. The animation shows a curious shift of the main rain fall from the southern quadrant to the north, as landfall occurs (see also the figure below). In trying to validate this, we found radar data at NCDC, presented is in the second plot below, which agrees with the MERRA distribution. However, and also likely related to the resolvable scales in the MERRA grid, the heaviest precipitation in MERRA is a larger distance away from the central low than observed.

While this seems like it is a reasonable representation of the real system, and likely useful, users must consider carefully the limitations in MERRA or any reanalysis data set when applying it to a project.

Figure 1. MERRA precipitation (color, mm/day) and sea level pressure (mb) at 12Z29AUG2005 with the complete NHC best track path for Hurricane Katrina.

Figure 2 Nexrad radar rainfall at 12:32Z29AUG2005.

March 1993 East Coast Snow

Recently, Midshipman S. Martin from the United States Naval Academy visited the GMAO, to learn about MERRA. The specific case study evaluated for a brief internship was the March 13, 1993 east coast snow storm (links to a recent Capital Gang discussion on the predictability of the storm). This was just a preliminary evaluation of how MERRA analyses represent the storm, in preparation for a senior paper.  As with the Feb 1979 storm (see the MERRA home page), we generated an animation (~8Mb) to get a sense of the storm track. 

Snowfall totals of 2 feet or more occurred at many observing stations. Below, the snowfall totals from Kocin et al (1995) are compared with MERRA. The northern extent of the heaviest snow seems to be a bit weak (in NY and western PA, for example) . The MERRA snow data was converted from snow water equivalent accumulated for the two days, and converted to snow depth using 10% snow/ice density.

At 12Z13MAR1993, the surface low was centered over Georgia, with the surface front extending southward through Florida. Aloft, the main part of the jet stream was North of the surface low, but a maximum in wind speed (likely a jet streak)was in the 300mb trough, lagging behind the surface front (below).

Looking closer at the vertical cross section through the trough and this wind maximum, we find a well defined tropopause fold associated with the 300 mb wind maximum. Below we compare the MERRA representation of the tropopause fold to a case study (1978) observed with aircraft measurements. The MERRA figure shows wind speed in black, potential temperature in dashed red and potential vorticity in shaded blue.

The main point here is that the MERRA analysis of the storm shows good dynamical structure of a very strong storm. More still would need done, evaluating the cyclogenesis, and how well the system physical processes through the lifecycle of the storm. However, this is one of the stronger examples of cyclogenesis in the MERRA period, and so another question is whether MERRA data can reproduce the dynamical structure of weaker storms. Ultimately it's a promising result so far.

Figures obtained from:

Keyser, Daniel. “Atmospheric Fronts:An Observational   Perspective.” In, Mesoscale Meteorology and Forecasting, 216–257.

Kocin, P., Schumacher, P., Morales, R., and Uccellini, L. (1995, February). Overview of the 12-14 March 1993 Superstorm. Bulletin of the American Meteorological Society, 76, 2, 165-182.

Hurricane Andrew, Aug 1992

MERRA Stream 2 has completed through 1993. We have been looking at various weather and climate events. Hurricane Andrew was a powerful, but fairly small hurricane. MERRA's 1/2 degree resolution is likely too coarse to adequately resolve the circulation, and there is no bogus or center relocation being done in the system. Still, assimilation of observations will show some circulation or feature.

There isn't much of a circulation prior to landfall in Southern Florida. Landfall was at 9Z24Aug1992. Figure 1 shows the sea level pressure and wind barbs from 6Z the closest analysis time before landfall. The pressure center is much higher than the observed center pressure (955 mb). the center of the pressure is located south of the best track at that time. The feature that really attracted attention is the offset of the wind circulation, even further south than the pressure center, and crossing the isobars at the center.

Figure 1 MERRA Sea Level Pressure and 1000 mb wind barbs from the 6Z 24 AUG92 analysis. The blue line shows the best track befre and after landfall, with red markers at 00Zs.

A closer look at the observations being assimilated shows that ERS1 did track over the center of circulation around 6Z. Figure 2 shows all the observations accepted into the analysis. There are ERS1 wind vectors crossing the center of the circulation, and the assimilation system accepted the data. The vectors closest the center are likely contaminated by precipitation, and should have been rejected. At this point, it's not clear how often this kind of problem occurs, or what might be done to detect and reject the bad data. It's under investigation.
An important point for reanalysis users, especially as resolutions are increased to better resolve weather and smaller scale circulations, is that reanalyses are assimilating vast quantities of observations. sometimes poor quality data does make it into the analyses. While quality of data and analyses are improving, users still need to consider that features may or may not be realistic. Likewise, we are providing some information on the accepted observations in MERRA. More difficult is providing access to the users on the actual observations.

Figure 2. MERRA analysis sea level pressure, analysis streamlines and windbarbs showing the accepted observations (red is buoy or ship, black is ERS1). Most of the ERS1 vectors seem to agree with the mass field, however, close to the center of the low pressure the ERS1 vectors are crossing the isobars. The wind analysis is drawing to the observations, even when they disagree with the mass field.

Brief Comparison with Interim Reanalysis

Recently the ECMWF Interim reanalysis has been released (see http://data.ecmwf.int/data/). We have a quick overlap and look at a comparison with MERRA at monthly time scales for January 1998.

The figure below shows two features of the MERRA system we have been tracking since the system was under development, 1) negative zonal wind bias in the tropics and extratropics and 2) dry bias in the lower troposphere, especially the tropics. These are apparent against each of the existing long reanalyses (see the quick look page for more comparisons). However, in comparing ERA40 and the new Interim reanalyses with MERRA, the magnitude of the differences is smaller compared to Interim.

At this time monthly files are not available on the ECMWF site. When those become available for download, we will integrate the Interim data into the quick look pages for comparisons.



(Gary Partyka, GMAO, downloaded the Interim data and performed the comparison and evaluation.)

MERRA Quick View

We've just opened a new link on the MERRA WWW page. It is a version of what our monitoring team is using to look quickly at various aspects of the circulation and water cycle. We'll be adding some other figures, like time series of the monthly data as well.

http://gmao.gsfc.nasa.gov/research/merra/prequel/view.php

Reanalysis Precipitation Climatology

On the MERRA WWW page, we are posting several figures showing the comparison of 5 satellite era reanalyses with GPCP and CMAP precipitation data sets. There are some similarities among the reanalyses, in their differences from the observations (Tropical precipitation, and interestingly European continental January precipitation), but also differences between the merged observation data sets (GPCP has lower tropical precipitation than CMAP, but higher January precipitation, in general). Citations are provided on the page, that provide some analysis and discussion on the sources of bias. However, there are many other aspects in comparing reanalyses to the observed data. These are only climatologies, so that interanual variability, weather scale and diurnal cycle differences are not expanded.

The WWW page is at: http://gmao.gsfc.nasa.gov/research/merra/reanalysis_precipitation_climatology.php

Please take a look, and feel free to make comments on this blog.

Summary of the MERRA User's Review Group Meeting

In late 2005 a MERRA review group was formed from experts in various aspects of Earth system science and users of existing reanalyses. Their charge was to review the GMAO strategy for MERRA and the validation experimentation and results, possibly contributing some of their own analysis. The goal for GMAO was to gain a preliminary assessment of the scientific merit of the GEOS-5 data assimilation system for MERRA prior to full production. In September 2007, the validation experiments began, and on November 7, the user review group met to discuss the results of the validation experiments with the GMAO and NASA HQ representatives.

The GMAO started the day, presenting a summary of the system and critical improvements in recent months (Rienecker), the dynamical circulation, clouds and radiation (Suarez and Bacmeister), climate variability features (monsoons, hurricanes, low level jets (LLJ) and diurnal cycle - Schubert) and precipitation statistics and land hydrology (Bosilovich and Koster). Key points from the presentations are summarized below.

Michele Rienecker reviewed some major and critical changes to the system since the inception of the Review Group. These include improvements in the use of retrieved wind speed over the ocean, improvement in the radiance assimilation (through the latest CRTM radiative transfer coefficients), corrections to bias and jumps in the radiosonde observations and a fix for diurnal cycle of glacier surface temperatures.

In looking at zonal circulation, Max Suarez showed the differences between the GEOS-5 and other reanalysis systems for winds, temperature and humidity. For example, the GEOS-5 eddy heights compare with ECMWF operational analysis both in a mean sense, and in the interannual variability. With small contour intervals in the zonal cross-sections, differences in tropopause height can be identified among all the reanalyses. In addition, GEOS-5 reproduction of stratospheric ozone profiles is reasonable, and a limited comparison of the beginning of a quasi-biennial oscillation looks promising. One possible systematic problem is high upper troposphere humidity (as compared to ECMWF and NCEP operational analyses). The radiation fluxes have some bias, as well, but these are somewhat reduced compared to the existing reanalyses (Figure 1).

Siegfried Schubert reviewed some evaluations of monsoonal circulations, including the North American monsoon and Indian monsoon. GEOS-5 reproduces the low level winds (e.g. the Somali jet and the Great Plains LLJ) as well as the subseasonal breaks observed in the monsoonal precipitation. There are some apparent regional biases in the precipitation, but this is also true among all the existing reanalyses. The GEOS-5 North American monsoon circulation and precipitation compare well with the North American Regional Reanalysis (NARR) (for July 2004, Figure 2). Globally, the interannual variations of precipitation compares well with observations, and better than existing reanalyses. In addition, the monthly average water budget shows globally averaged analysis tendencies to be a small value (Figure 3). However, the diurnal amplitude of continental precipitation is large and the phase is shifted to a daytime maximum compared to observations. This is a problem for all reanalyses, and it persists in the GEOS-5 system.

Mike Bosilovich reviewed monthly mean precipitation, where GEOS-5 generally produces good fields compared with GPCP and CMAP, not only in the global mean, but also spatial correlation. In addition global P-E is generally small (near zero) indicating that the global analysis is relatively well balanced (but will be non zero). The GEOS-5 precipitation is reasonable in many regions and latitude bands. Comparisons for the Mississippi River basin precipitation against daily gauge data show the GEOS-5 was able to produce the daily precipitation events, and the no-rain days for Jan-Oct 2004 (Figure 4). However, maximum intensities in the warm season are underestimated, leading to an underestimate of the total basin precipitation. Randy Koster’s analysis of the time series of precipitation shows that the occurrence of rain during the day coincident with solar forcing causes high interception loss of water vapor, and then the runoff water is underestimated. The transition of the observing system to include SSM/I was tested in a data withholding experiment. GEOS-5 tropical precipitation increases with the inclusion of SSM/I, but the increase is less than 10% of the tropical precipitation (in contrast, JRA25 has a change in extratropical precipitation). There is also a small increase of total column water, ocean surface winds and ocean evaporation.

The overall conclusion is that the GEOS-5 system can produce many aspects of the Earth system as well or better than existing reanalyses. The quality of the data coupled with the fine temporal and spatial scale of the data should make the GEOS-5 reanalysis useful for many purposes. While there were spirited discussions among all the participants, the external user group members’ sentiment reflects this conclusion as well. As of November 2007, the reanalysis data streams are being spun up, and data should start flowing to the scientific community early in 2008. The full MERRA data product will take approximately 18 months to generate.


Figure 1 Monthly mean (Jan 2004) TOA Longwave radiation differences between CERES ERBE-like observations and several reanalyses and operational analyses.

Figure 2 Comparison of the seasonal evolution of the North American monsoon between the North American Regional Reanalysis (NARR) and GEOS5.

Figure 3 Global vertically integrated water vapor budget for July 2004 including the physical components, the analysis increment and residual.

Figure 4 Mississippi River basin area-average (over all sub-basins) daily precipitation for January – September 2004. The figures show the scatter of the daily data, the daily time series, and the accumulated precipitation. The observations are CPC daily ¼ degree gridded gauge data.

Status Nov 9

The validation of the system has been somewhat time consuming between this post and the previous, and much has happened. At least 20 GMAO staff (or more) spent several weeks interogating the validation experiments each focusing on various Earth system components. On Oct 11, the GMAO held an internal review of the validation experiments. On Nov 7, the summary of these results were presented to our User Review group in a meeting at GSFC. My interpretation of the Review is that the system has more than enough scientific merit to proceed to production phase, weighing the advances and advantages against the limitations and some weaknesses. When any formal writing from the Review are made available for public posting, I'll put it on the blog. This is a significant milestone for the MERRA project and the GMAO.

There were many very positive results that came out of the MERRA validation experiments. Too many to easily synthesize into blog posts. A validation document is under development, but should take some time. Some results will be posted here as time goes on. In the near term, however, validation pointed out a serious flaw in the system. When the CERES science team evaluated the data, they found that Antarctica and glaciers did not have a diurnal cycle of surface temperature. The reason ended up being a thick glacier layer. Some new code, including a thinner layer and revisions to the energy budget code have produced very reasonable results. So, this fix will be added to the MERRA system. (see the Figure)

Figure: Time series of 2m air temperature at two Antarctica stations. The green line indicates GEOS5 Patch 15, Blue is patch 20 (including the fix) and the read is ECMWF operational analysis. Model data are the nearest gridpoint to the stations. Station data is marked with a black box.

So, the spin up of MERRA production runs are on hold until the system is updated. Some testing of convection parameterization coefficients has been going on through this process. A decision is pending on which, if any, will go into MERRA. The issue to be resolved are, updating the system with new glacier surface temperatures, finalizing the MERRA output routines and final evaluation of the convection parameterization. Spinup runs will restart once these issues are resolved.

Ocean Surface Winds and Fluxes

The ocean atmosphere interactions are one of the crucial elements in climate variability. The MERRA system does not have a coupled ocean model and data assimilation, but future reanalyses will likely go in this direction. In MERRA, seas surface temperatures are prescribed and ocean surface wind observations (from buoys and satellites) are analyzed. Downward components of the radiation would be related to the parameterized clouds and radiation calculations, as well as the input observations (radiances, temperature and moisture). The following figures prepared for validation compare some winds and fluxes with GEOS5 experiments and other reanalyses.

This following figure shows the daily time-series for Jan 2004 and 2006 U10M and V10M winds at the TAO mooring location of 165E on the equator. GEOS-5 shows good agreement with TAO and matches the minimum and maximum values everywhere. In Jan 2004 and 2006, GEOS-5 is more highly correlated with QSCAT than NCEP CDAS or JRA-25. The NCEP-CDAS analysis shows several periods of larger bias against the observations.

The next figure shows maps of monthly latent heat flux for GEOS5, JRA-25 and NCEP CDAS. GEOS-5 has much less evaporation out of the ocean than JRA-25 and NCEP CDAS, especially in the western boundary currents: Gulf Stream and Kuroshio. Mean and RMS differences between GEOS-5 and JRA-25 and GEOS-5 and NCEP CDAS are much larger than that of NCEP CDAS and JRA-25 in the above region. Similar patterns are seen in January 2004.

In three validation periods investigated so far (Jan/Jul2004 and Jan 2006), GEOS-5 net radiation is more highly correlated with TAO in the Eastern Pacific that other reanalyses.
NCEP-CDAS generally is biased low in most time-periods and TAO locations. GEOS5 also correlates well with the TAO incoming shortwave radiation observations.


The three reanalyses are fairly different in their net heat fluxes. GEOS-5 has less heat loss than both NCEP and JRA in the Kuroshio and Gulfstream areas, and more heat gain off Western Australia. In the 45S-45N band, GEOS5 and JRA have substantial differences.