Reanalysis Session at Fall AGU Absstracts due soon

Reanalysis: Evaluation and Intercomparison

Submit an Abstract to this Session Session ID#: 3181

Session Description:

Reanalyses are consistent data sets which represent the past state and variability of geophysical systems. Reanalyses are frequently used as "observations" or "true state" when assessing the fidelity of model simulations of the recent past, and also in studying short-term variability and long-term changes of the system. However, inhomogeneities in the observing system and biases in the numerical models may lead to spurious changes and trends. These issues can be addressed to some extent by intercomparing the output from different reanalyses, and comparing reanalysis output to independent observations. In the near future, organized efforts such as the SPARC Reanalysis Intercomparison Project (S-RIP) and the EU funded project UERRA are planning to systematically evaluate reanalysis products.

The session welcomes contributions on the evaluation and intercomparison of reanalyses, including global, regional, atmospheric, ocean, and coupled reanalyses. In particular, intercomparison of multiple reanalyses and comparisons with independent (non-assimilated) data are encouraged.

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.

Data Servers Online

MERRA and other data servers are online following the US federal government shutdown. If any difficulties or problems are encountered, use the appropriate contact information to report the issue.

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.

Data Servers Offline

MERRA data servers, along with all US federal government data servers, not required for the defense of life and property, will be offline while the government is shut down. Changes in their status will be posted.

Output Data Review

We are currently reviewing the MERRA File Specification Document, and considering new variables and diagnostics that should be incorporated into future experiments and reanalyses. This is directed toward information that needs to be captured during run time, as opposed to diagnostics that can be post processed from existing data. For example, to get Max/Min temperature from MERRA would be the maximum/minimum hourly averaged temperature, in the current configuration. We will implement for future analyses hourly maximum and hourly minimum temperatures, so that the instantaneous Max/Min T can be captured from the system.

Any suggestions would be welcome. Please include any computations that may be non standard, and reasons or applications of the suggested diagnostic.

Apologies for letting the blog become stagnant. While I have seen a lot of interesting work with reanalyses and MERRA, finding the time to work up a worthwhile post is remarkably challenging.

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.