# Presentation: 321079

## Reanalysis Issues

- Vernon E. Kousky
- And
- John E. Janowiak
- Climate Prediction Center
- 7 August 2003

## Decadal Trends in CDAS/Reanalysis

**Real?**
**Due to non-homogeneities in the observational data sets?**
**Partly real and partly due to non-homogeneities in the observational data sets?**
** ****	****Future reanalysis efforts should attempt to answer these questions.**

## Example: Global Temperature Anomaly wrt 1979-1995

- There are notable differences in the character of the anomalies between the period prior to 1979 (pre-satellite sounding data) and the period 1979-present (period with satellite sounding data). There is a stronger annual cycle and biases in temperature at all levels prior to 1979.

**P**

## Example: Global 200-hPa HT Anomaly wrt 1979-1995

- There are notable differences in the character of the height anomalies between the period prior to  and after the late 1970s. Is the apparent climate jump in the late 1970s real, a result of a changing data base or partly real and partly data base related? What is the truth?

## Example: Global 700-hPa RH Anomaly wrt 1979-1995

- There is a pronounced downward trend in 700-hPa relative humidity anomalies during the period of record. How much of this trend is real? Also evident is a strong annual cycle in RH anomalies prior to 1979, which is related to the enhanced annual cycle of temperature shown previously. Changes in the observational data base are probably responsible for these features.

## Example: Global pattern of 700-hPa RH Anomalies

- When spatial patterns of the RH anomalies were plotted bulls-eyes were noted in some areas. Most notable was the bulls-eye in the Gulf of Alaska for the period 1961-1978, which is centered on the position of where weather Ship 4YP was located. 4YP was in operation from the early 1960s until 1981. Other bulls-eyes are apparent over islands in the South Atlantic, Indian Ocean and south of the southern tip of Africa.

## RH Diurnal Cycle: 1961-1978

**00UTC**

**06UTC**

**12UTC**

**18UTC**

- At the location of ship 4YP the bulls-eyes are strongest at 00UTC and 12UTC (hours when soundings were taken). This pattern represents the difference between observed RH (which may have errors) and model climatology (which may have biases).

## Relative Humidity at location of Ship 4YP

- RH is less during period when Ship 4YP was taking soundings. Does this indicate that the RH is too high in the recent record (probably the case) or that the soundings were in error?

## Trends: Reality or Fiction?

**Compare CDAS to AMIP run of GFS (NCEP’s operational global model)**
**Height and Zonal Wind**

## 500-hPa Height Anomalies: 30N-30S

- GFS-AMIP shows a gradual rising trend throughout the period, whereas CDAS shows a jump in the late 1970s. The trend in CDAS appears  to be exaggerated due to changes in the observational data base (satellite data).

## 500-hPa Height Anomalies: 30N-90N

- Low-frequency variability is quite different in the GFS/AMIP run  compared to that in the CDAS analyses, especially for the first half of the record. Since SST forcing is the same in the two analyses, what are the causes for the differences in character?

## GFS-CDAS/Reanalysis 200-hPa Height Differences (1951-2000)

- Operational GFS model, run with observed SSTs (AMIP mode, no atmospheric observations included), has a cold bias with respect to CDAS at all latitudes, resulting in lower 200-hPa heights.

## GFS-CDAS/Reanalysis 200-hPa Zonal Wind Differences (1951-2000)

- Operational GFS model features a poleward shift of the midlatitude jet streams wrt CDAS.

## Summary: Trends

- CDAS contains considerable low-frequency variability, which may or may not be real
- Significant differences exist between CDAS and GFS/AMIP analyses
- Model biases together with data base non-homogeneities are probable causes for most of these differences
- Mean differences in height and temperature indicate that the GFS has a strong cold bias
- This bias is important in data sparse regions,
- In order to address important scientific issues regarding decadal variability, this bias needs to be reduced prior to future reanalyses

## Precipitation

**Compare GFS and CDAS to GPCP analyses**
**Note: GPCP analyses are obtained by merging observed precipitation with satellites estimates of precipitation.**

## Global Precipitation

**GPCP**

## Global Precipitation Differences

## Global Precipitation  

- Precipitation anomalies(top) between Reanalysis & GPCP agree well – not so well after 1996.  GFS anomalies agree over entire period.  Mean rainfall in both Reanalysis & GFS  (bottom) exhibits a spuriously vigorous seasonal cycle.  GFS has ~25% greater precipitation than GPCP.

## Precipitation  (Monsoon)

- Overly vigorous monsoon precip. in Asia in Reanalysis (NH, left).  Over SH Austral-Asia, Reanalysis & GPCP compare well.  Problems with surface parameterization or  modeling of the diurnal cycle?  Asymmetry between NH & SH leads to annual cycle in global precip (previous slide).

- NH

- SH

## Summary: Precipitation

- GFS is generally too wet when compared to GPCP.
- Annual cycle in GFS appears to be too strong.
- GFS does show some improvement in the character of some features relative to CDAS/Reanalysis.

## CPC perspective on Reanalysis

**Possible Benefits to CPC:**
- 1. Improved Monitoring
- 2. Understanding and documenting long-term trends
- 3. Improved week-2 predictions
- 4. Improved seasonal predictions
- 5. Enhanced observational data base as a byproduct for doing the reanalysis

## CPC perspective on Reanalysis (cont.)

**However, to be able to realize above benefits, we need to:**
- 1. Reduce/remove models biases to improve pre-1979 analyses
- 2. Conduct research to deal with data discontinuity issues (pre and post satellite era)
- 3. Enhance global data mining/rescue efforts

## CPC perspective on Reanalysis (cont.)

**CPC involvement will also require human and computing resources**
- 1. Additional  ??  FTEs (contracted support)
- 2. Computing resources:  Disk space, file servers