Highlights from Project Minerva: Representation of the South Asian Monsoon
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1 Highlights from Project Minerva: Representation of the South Asian Monsoon COLA team: Ben Cash, lead Eric Altshuler Rondro Barimalala Paul Dirmeyer Mike Fennessy Abheera Hazra Jim Kinter V. Krishnamurthy Julia Manganello David Straus University of Oxford: Tim Palmer Andrew Dawson ECMWF team: Frederic Vitart, lead Roberto Buizza Erland Kallen Franco Molteni Tim Stockdale Peter Towers Nils Wedi Many thanks to NCAR for their ongoing support
2 Project Minerva Explore the impact of increased atmospheric resolution on model fidelity and prediction skill in a coupled, seamless framework Follow on to Project Athena, which focused on uncoupled numerical weather prediction models on climate timescales Initial integrations made as part of NCAR Advanced Scientific Discovery Program to inaugurate new Yellowstone System. Systematically evaluate the prediction skill and reliability of a robust set of hindcast ensembles at low, medium and high atmospheric resolutions State-of-the-art ECMWF coupled operational long-range prediction system System Atmosphere model cycle Atmosphere resolution Atmosphere vertical levels Ocean model Ocean resolution Ocean vertical levels MINERVA IFS cy 38r1 T319 (64km) / T639 (32km) / T1279 (16km) 91 levels, top = 1 Pa NEMO v 3.0/3.1 1 degree, ~ 0.3 deg. Lat 42 levels
3 Minerva Catalog Resolution Start Dates Ensembles Length Period of Integration T319 May 1, Nov months Nov months ** T639 May 1, Nov months May 1, Nov (4) months Nov months T1279 May 1, Nov months Over 40 million Yellowstone CPU hours Approaching 2 petabytes of data Analysis of ENSO, ISOs, Tropical Cyclones, NAO, MJO, blocking
4 Project Minerva Highlights WWOSC Montreal August 2014 Ben Cash Precipitation: Summer 2010 (IFS 16km)
5 South Asian Monson in Minerva JJAS Rainfall T1279 (16 km) EIMR GB BD WG BB
6 South Asian Monson in Minerva JJAS Rainfall T319 (64 km) EIMR GB BD WG BB
7 T1279: km TRMM: km T319: km GPCP: km
8 Minerva Catalog Resolution Start Dates Ensembles Length Period of Integration T319 (64 km) May 1, Nov months Nov months ** T639 (32 km) T1279 (16km) May 1, Nov months May 1, Nov (4) months Nov months May 1, Nov months
9 Minerva Catalog Resolution Start Dates Ensembles Length Period of Integration T319 (64 km) T639 (32 km) T1279 (16km) May 1, Nov months Nov months ** May 1, Nov months May 1, Nov (4) months Nov months May 1, Nov months Low resolution comparable in resolution of features to TRMM 1734 JJAS season in T319 simulations alone Ensembles are not continuous runs restart every May 1 How to compare this to our single, continuous realization of reality?
10 Regional Rainfall Patterns GPCP Correlation of EIMR index with rainfall at every other point Some regions show no correlation with EIMR Some regions not actually correlated Patterns generally similar, but differ in details and magnitude T319 ensemble mean Not a direct comparison: 51- member mean versus one observational record How sensitive is the observed pattern to noise?
11 One approach: Create synthetic observed time series by drawing one member per year Each path represents an equally plausible representation of the model climate May-Nov 1980 May-Nov 1981 May-Nov May-Nov 2011 JJAS JJAS JJAS JJAS JJAS JJAS 0 1 JJAS JJAS JJAS JJAS JJAS 1 2 JJAS JJAS JJAS JJAS JJAS 2 3 JJAS JJAS JJAS JJAS JJAS 3 4 JJAS JJAS JJAS JJAS JJAS 4 5 JJAS JJAS JJAS JJAS JJAS JJAS JJAS JJAS JJAS JJAS 50
12 For sake of simplicity, initial efforts draw same member for each year May-Nov 1980 May-Nov 1981 May-Nov May-Nov 2011 JJAS JJAS JJAS JJAS JJAS JJAS 0 1 JJAS JJAS JJAS JJAS JJAS 1 2 JJAS JJAS JJAS JJAS JJAS 2 3 JJAS JJAS JJAS JJAS JJAS 3 4 JJAS JJAS JJAS JJAS JJAS 4 5 JJAS JJAS JJAS JJAS JJAS JJAS JJAS JJAS JJAS JJAS 50
13 Impact of sampling T319 correlation Repeat analysis for each synthetic record Count number of records that are correlated at 90% level for each point Green = all members Significance count
14 ENSO-Monsoon Correlation: JJAS EIMR and SST ENSO and EIMR negatively correlated in the observations, weakening in recent decades Ensemble mean shows very strong negative correlation EIMR and ENSO in ensemble mean Significance count
15 Model Mean EIMR-NINO34 correlation: All records Observed Ensemble mean
16 Conclusions Minerva: Lessons Learned Dedicated computing resources allow for massive and efficient generation of data Perhaps too efficient Verification of modern models is an ever-increasing challenge Low resolution model in our study is ~0.5 degree GPCP is 2.5 degrees Higher resolution products are limited in coverage or duration Great value in expansion of ensemble in combination with increased resolution Monsoon Model capably reproduces climatology of seasonal mean Ensemble-mean regional association patterns similar to observations Sampling variability introduces tremendous range in diagnosed ENSOmonsoon relationship Difficult null hypothesis to reject for any observed change in strength Little reason to expect value to be constant in time Implications for statistical forecasts?
17 Extra Material
18 Minerva Catalog Resolution Start Dates Ensembles Length Period of Integration T319 (64 km) May 1, Nov months Nov months ** T639 (32 km) T1279 (16km) May 1, Nov months May 1, Nov (4) months Nov months May 1, Nov months
19 Skill concentrated over tropical Pacific, low over land
20 General but weak expansion of skillful region at 16km
21 Prediction skill over India Some improvement with resolution, but large gaps remain
22 T1279: TRMM: T319: GPCP:
23 GPCP Regional Rainfall Patterns Large scale indices (AIR, EMIR) may not represent regional rainfall totals Regional variations directly impact agriculture, disease transmission, etc. Correlate regional index with rainfall at every other point Project Minerva Highlights WWOSC Montreal August 2014 Ben Cash
24 GPCP T319 Ensemble Mean Patterns generally similar, but differ in details and magnitude Not a direct comparison: 51-member mean versus one observational record How sensitive is observed pattern to noise?
25 Test for impact of sampling Repeat analysis for each synthetic record Count number of records that are correlated at 90% level for each point Green = all members
26 Project Minerva Partners ECMWF team: Frederic Vitart, lead Roberto Buizza Erland Kallen Franco Molteni Tim Stockdale Peter Towers Nils Wedi COLA team: Ben Cash, lead Eric Altshuler Rondro Barimalala Paul Dirmeyer Mike Fennessy Abheera Hazra Jim Kinter V. Krishnamurthy Julia Manganello David Straus University of Oxford: Tim Palmer Andrew Dawson
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