915
Figure 1: Satellite map of the New York Metropolitan Region. Shown on the map are the 916
Central Park weather station (circle) and The Battery tide gauge (triangle). Source: ESRI World 917
Imagery.
918 919
Figure 2: a) Temperature change (°C) and b) precipitation change (%) for the 2080s timeslice 920
relative to the 1970-1999 model baseline, A1B emissions scenario and 16 GCM ensemble mean.
921 922
Figure 3: a) Mean annual temperature for the New York City region, (ºC), 1970-1999 in each of 923
the 16 GCMs, GCM ensemble, Central Park station data and Reanalysis (see methods section for 924
more information). Also shown as hash marks is the interannual standard deviation about the 925
mean for each of the 19 products. b) monthly mean temperature for the New York City region, 926
(ºC), 1970-1999. The two observed products, the GCM ensemble average, and four points in the 927
GCM distribution (lowest, 17th percentile, 83rd percentile, and highest) are shown. c) Mean 928
annual precipitation for the New York City region, (cm), 1970-1999 in each of the 16 GCMs, 929
GCM ensemble, and Central Park observations. Also shown as hash marks is the interannual 930
standard deviation about the mean for each of the 18 products. d) monthly mean precipitation for 931
the New York City region, (cm), 1970-1999. Central park observations, the GCM ensemble 932
average, and four points in the GCM distribution (lowest, 17th percentile, 83rd percentile, and 933
highest) are shown.
934 935
Figure 4: Daily distribution (number of days per year) of: a) all-year mean, b) summer (June-936
August) maximum, and c) winter (December-February) minimum temperature anomalies (ºC), 937
1980-1999 for Central Park observations (black line) and three GCMs (CSIRO, GISS, and MPI 938
ECHAM5).
939 940
Figure 5: Daily distribution (number of days per year) of precipitation (mm), 1980-1999 for 941
Central Park observations (black line) and three GCMs (CSIRO, GISS, and MPI ECHAM5). The 942
first bin, containing less than 10 mm, is not shown.
943 944
Figure 6: Combined observed (black line) and projected: a) temperature (ºC) and b) annual 945
precipitation (mm). Projected model changes through time are applied to the observed historical 946
data. The three thick lines (red, green, and blue) show the ensemble average for each emissions 947
scenario across the 16 GCMs. Shading shows the central 67 % range across the 16 GCMs and 3 948
emissions scenarios. The bottom and top lines, respectively, show each year’s minimum and 949
maximum projections across the suite of simulations. A ten-year filter has been applied to the 950
observed data and model output. The dotted area between 2003 and 2015 represents the period 951
that is not covered due to the smoothing procedure.
952 953
Figure 7: Seasonal a) temperature change (ºC) and b) precipitation change (%) projections, 954
relative to the 1970-1999 model baseline, based on 16 GCMs and 3 emissions scenarios. The 955
maximum and minimum are shown as black horizontal lines; the central 67% of values are 956
boxed, and the median is the thick line inside the boxes.
957 958
Figure 8: Daily distribution (number of days per year) of winter (December-February) minimum 959
temperature anomalies (ºC), for the New York Metropolitan Region in the CSIRO GCM. Black 960
line, 1980-1999 hindcast; dotted line, 2080-2099 A1B scenario.
961
TABLE 1. Acronym, host center, atmosphere and ocean grid box resolution, and reference for the 16 GCMs used in the analysis.
962
Model Acronym
Institution Atmospheric
Resolution (Lat x Lon )
Oceanic Resolution (Lat x Lon)
References
BCCR Bjerknes Center for Climate Research, Norway 1.9 x 1.9 0.5 to 1.5 x 1.5 Furevik et al., 2003 CCSM National Center for Atmospheric Research, USA 1.4 x 1.4 0.3 to 1.0 x 1.0 Collins et al., 2006
CGCM
Canadian Center for Climate Modeling and Analysis, Canada
2.8 x 2.8 1.9 x 1.9 Flato 2005
CNRM
National Weather Research Center, METEO-FRANCE, France
2.8 x 2.8 0.5 to 2.0 x 2.0 Terray et al., 1998
CSIRO CSIRO Atmospheric Research, Australia 1.9 x 1.9 0.8 x 1.9 Gordon et al., 2002 ECHAM5 Max Planck Institute for Meteorology, Germany 1.9 x 1.9 1.5 x 1.5 Jungclaus et al., 2005
ECHO-G
Meteorological Institute of the University of Bonn, Germany
3.75 x 3.75 0.5 to 2.8 x 2.8 Min et al., 2005
GFDL-CM2.0 Geophysical Fluid Dynamics Laboratory, USA 2.0 x 2.5 0.3 to 1.0 x 1.0 Delworth et al., 2006 GFDL-CM2.1 Geophysical Fluid Dynamics Laboratory, USA 2.0 x 2.5 0.3 to 1.0 x 1.0 Delworth et al., 2006
GISS NASA Goddard Institute for Space Studies 4.0 x 5.0 4.0 x 5.0 Schmidt et al., 2006
INMCM Institute for Numerical Mathematics, Russia 4.0 x 5.0 2.0 x 2.5
Volodin and Diansky, 2004
IPSL Pierre Simon Laplace Institute, France 2.5 x 3.75 2.0 x 2.0 Marti, 2005
MIROC Frontier Research Center for Global Change, Japan 2.8 x 2.8 0.5 to 1.4 x 1.4 K-1 Developers, 2004
MRI
Meteorological Research Institute, Japan
2.8 x 2.8 0.5 to 2.0 x 2.5
Yuikimoto and Noda, 2003
PCM National Center for Atmospheric Research, USA 2.8 x 2.8 0.5 to 0.7 x 1.1 Washington et al., 2000
UKMO-HadCM3
Hadley Center for Climate Prediction, Met Office, UK 2.5 x 3.75 1.25 x 1.25 Johns et al., 2006
963
TABLE 2. Checklist of questions to inform selection of climate hazard assessment and projection methods 964
Question Possible implication for choice of method, plus NYC context
1. Are high quality historical data available for a long time period? When little high-quality historical climate data are available, options for projections are extremely limited. Records of at least several decades are needed to sample the range of natural
variability. As RCMs continue to improve, use of raw outputs from RCMs may increasingly be used in such regions, since bias correction and statistical approaches are not feasible without historical climate data. This was not an issue in data-rich NYC.
2. Are projections needed for the entire 21stcentury? If yes, this may preclude RCMs due to computational expense.
This was an important consideration for NYC, since some sectors such as telecommunications were focused on the 2020s timeslice, while others such as Port Authority of New York and New Jersey manage infrastructure expected to last until 2100.
3. Are multiple emissions scenarios needed, for example to emphasize how mitigation can compliment adaptation?
If yes, RCMs may not be the best approach, since computational expense generally precludes the use of more than 1-2 scenarios.
This was an important consideration in New York City, since the adaptation effort was part of a broader sustainability effort (PlaNYC) that embraced greenhouse gas mitigation.
4. Are a large group of GCMs and initializations required, in order to sample a broad range of global climate sensitivities and estimates of within-GCM variability, respectively?
If yes, RCMs may not be the best approach, since computational expense generally precludes the use of more than a few GCMs or GCM initializations per RCM. New York City stakeholders expressed interest in the full range of GCM sensitivities.
5. What climate variables are needed, and are they available at the necessary spatial and temporal resolution within public climate model archives?
In NYC, relatively few variables were needed and subdaily information was not required. Additional variable needs at subdaily resolution might argue for the use of RCM archives such as NARCCAP as they continue to be populated, instead of archives such as the first generation of BCSD (monthly temperature and precipitation only). While use of public climate model archives minimizes cost and time, even archived outputs generally require at least some bias and/or scale correction and post-processing for stakeholder applicability.
6. What level of resources are available, and in what time frame is the information needed?
Region and question specific tailored downscaling efforts, as opposed to use of archived downscaled products, may not be possible when resources and time are limited. While NYC had substantial resources available, the short time frame (~8 months) precluded developing new tailored downscaling.
7. Are projections needed for a single in-depth sectoral application and variable in one municipality, or does a large multisectoral and pan-regional group of stakeholders need a coordinated set of scenarios covering a series of standard variables?
In tailored statistical downscaling the method is optimized to the particular location and/or variable. When many variables and a larger region are included, no single optimization method will generally be best for all variables and locations, potentially leading to inconsistencies in either methods or projections across variables and locations. In NYC, the initial emphasis was on generating a common denominator of consistent scenarios based on consistent methods (the delta method) to facilitate coordination across 40 stakeholder entities.
8. Are high-frequency climate inputs that are continuous in time and If an impacts model is to be run with climate outputs, the range of
space required, such as for input into an impacts model? (e.g., a hydrological model to assess turbidity)
climate and impact results (rather than just the ‘delta’ mean) will likely be of interest, which may argue for a downscaling
technique that allows variance to change, such as BCSD.
Statistical downscaling techniques that include weather generators (such as SDSM) may be desirable to create a long record at the needed resolution that includes a range of extreme outcomes for planning purposes. The larger the continuous geographic domain (e.g., a large watershed) the greater the need for caution regarding weather generator treatment of spatio-temporal correlation.
While impacts modeling was not the initial thrust of the NYC CCATF effort, climate scenarios for impact modeling are being developed for specific sectors (e.g., NYCDEP, 2008).
9. Is the region’s climate characterized by large spatial heterogeneity?
If not, applying the delta method to a single GCM gridbox may be justifiable for many applications, as it was in NYC.
10. Are modes of variability important and predictable? If not, the use of 30-year time slices (and the delta method) that emphasize the signal of greenhouse gases and other radiatively
important agents should be emphasized, as it was in NYC.
965 966
TABLE 3. NCAR CCSM climatology of available simulations and CCSM ensemble for the 967
gridbox covering New York City: a) 1970-1999 hindcast; b) A1B 2080s (2070 to 2099 average) 968
relative to the same-simulation 1970 to 1999 hindcast.
969 a) 970
1970 - 1999 Mean temperature
(°C)
1970 - 1999 Mean precipitation
(cm)
CCSM Run1 9.38 98.03
CCSM Run 2 9.27 91.88
CCSM Run 3 9.67 92.08
CCSM Run 5 9.42 94.87
CCSM Run 6 9.64 95.22
CCSM Run 7 9.64 91.30
CCSM Run 9 9.68 94.69
CCSM Ensemble
9.53 94.10
971 b) 972
2080s A1B Temperature
change (°C)
2080s A1B Precipitation
change (%)
CCSM Run1 3.44 2.81
CCSM Run 2 3.32 10.15
CCSM Run 3 3.03 12.44
CCSM Run 5 3.24 9.75
CCSM Run 6 2.75 9.56
CCSM Run 7 2.96 12.03
CCSM Run 9 3.01 10.36
CCSM Ensemble
3.11 9.52
TABLE 4. Annual and seasonal temperature (a,b,( °C decade-1)) and precipitation (c,d, (cm 973
decade-1) ) trends, and 20th century (a,c) and 1970-1999 (b,d). Shown are observed Central Park 974
station data, the 16 GCM ensemble, and four points on the GCM distribution (lowest, 17th 975
percentile, 83rd percentile, and highest).
976 a) 977
20th century*
Min 16% 83% Max Ensemble Observed
Annual -0.03 0.02 0.12 0.17 0.07** 0.15**
DJF -0.04 0.02 0.16 0.19 0.08** 0.20**
MAM -0.05 -0.02 0.12 0.25 0.06** 0.18**
JJA -0.02 0.03 0.11 0.15 0.07** 0.12**
SON 0.00 0.03 0.15 0.18 0.09** 0.08
978 b) 979
1970 - 1999 Min 16% 83% Max Ensemble Observed
Annual -0.11 0.10 0.28 0.39 0.18** 0.21
DJF -0.47 -0.05 0.35 0.51 0.11 0.76
MAM -0.36 -0.15 0.41 0.74 0.14 0.10
JJA -0.01 0.13 0.29 0.44 0.20** 0.05
SON -0.06 0.13 0.50 0.70 0.29** -0.03
980 981 982
c) 983
20th
century* Min 16% 83% Max Ensemble Observed
Annual -1.22 -0.22 0.66 0.76 0.16 1.60
DJF -0.23 -0.18 0.27 0.78 0.05 0.27
MAM -0.27 -0.13 0.28 0.39 0.10 0.90
JJA -0.69 -0.39 0.22 0.35 -0.07 -0.09
SON -0.25 -0.08 0.32 0.46 0.10 0.61
984 d) 985
1970 - 1999 Min 16% 83% Max Ensemble Observed
Annual -3.52 0.02 2.05 5.73 0.87 -1.77
DJF -3.21 -0.19 1.48 2.94 0.48 -0.48
MAM -2.33 -1.37 1.05 1.98 -0.08 1.55
JJA -2.08 -1.33 1.19 1.75 -0.03 -1.51
SON -1.72 -0.55 1.89 2.93 0.48 -1.72
986 987
*Only 15 GCMs were available for the 1900-1999 hindcast.
988
**Trend is significant at the 99% level.
989 990 991 992
TABLE 5. Mean annual changes in temperature and precipitation for New York City.*
993
2020s 2050s 2080s
Air temperature ** + 0.8 to 1.7° C + 1.7 to 2.8° C + 2.2 to 4.2° C
Precipitation ** + 0 to 5 % + 0 to 10 % + 5 to 10 % 994
* Based on 16 GCMs and 3 emissions scenarios.
995
** Shown is the central range (middle 67%) of values from model-based distributions;
996
temperatures ranges are rounded to the nearest tenth of a degree and precipitation to the nearest 997
5%.
998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010
TABLE 6. Sea level rise projections for New York Citya 1012
2020s 2050s 2080s
IPCC-basedb, + 5 to 13 cm + 18 to 30 cm + 30 to 54 cm Rapid ice-melt scenariob,c ~ 13 to 25 cm ~ 48 to 74 cm ~ 104 to 140 cm 1013
a Based on 7 GCMs and 3 emissions scenarios.
1014
b Shown is the central range (middle 67%) of values from model-based distributions rounded to 1015
the nearest cm.
1016
c Rapid ice-melt scenario is based on recent rates of ice melt in the Greenland and West Antarctic 1017
Ice sheets and paleoclimate studies. See text for details.
1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031
TABLE 7. Extreme Events Projections.
1032
Extreme Event Baseline (1971- 2000)
Average duration (in days)
2 minimum temperature at or below 32°F (0°C)
72 53 to 61 45 to 54 36 to 49
1-in-10 yr flood to reoccur, on average
associated with 1-in-10 yr flood
1.9 2.0 to 2.1 2.1 to 2.2 2.3 to 2.5 Coastal
floods &
stormsd,e
1-in-100 yr flood to reoccur, on average
a Shown is the central range (middle 67%) of values from model-based distributions based on 16 GCMs 1033
and 3 emissions scenarios.
1034
bDecimal places shown for values less than 1 (and for all flood heights) 1035
cDefined as 3 or more consecutive days with maximum temperature exceeding 90°F (~32°C).
1036
dDoes not include the rapid ice-melt scenario.
1037
e Shown is the central range (middle 67%) of values from model-based distributions based on 7 1038
GCMs and 3 emissions scenarios.
1039
Flood heights (in m) associated with 1-in-100 yr flood
2.6 2.7 to 2.7 2.8 to 2.9 2.9 to 3.2
TABLE 8. Global climate model and regional climate model pairings used from NARCCAP 1040
Global Climate Model Driver
Regional Climate Model Combination
RCM Reference Geophysical Fluid
Dynamics Laboratory (GFDL)
Regional Climate Model Version 3 (RCM3) Coupled Climate Model (CGCM3)
Regional Climate Model Version 3 (RCM3) Coupled Climate Model (CGCM3)
Canadian Regional Climate Model (CRCM)
CRCM + CGCM3
Caya and Laprise, 1999
Hadley Centre Coupled Model, Version 3 (HadCM3)
Hadley Regional Model 3 / Providing Regional Climates for
Impacts Studies (HRM3)
HRM3 + HadCM3
Jones et al., 2004
1041
1042
1043 _ 1044
Figure 1: Locations of the Central Park weather station (circle), and The Battery tide gauge 1045
(triangle), and the 5 boroughs of New York City. Source: ESRI World Imagery.
1046 1047 1048
1049 _ 1050
Figure 2: a) Temperature change (°C) and b) precipitation change (%) for the 2080s timeslice 1051
relative to the 1970-1999 model baseline, A1B emissions scenario and 16 GCM ensemble mean.
1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065
1066
1067 _ 1068
Figure 3: a) Mean annual temperature for the New York City region, (ºC), 1970-1999 in each of 1069
the 16 GCMs, GCM ensemble, Central Park station data and Reanalysis (see methods section for 1070
more information). Also shown as hash marks is the interannual standard deviation about the 1071
mean for each of the 19 products. b) monthly mean temperature for the New York City region, 1072
(ºC), 1970-1999. The two observed products, the GCM ensemble average, and four points in the 1073
GCM distribution (lowest, 17th percentile, 83rd percentile, and highest) are shown.
1074 1075
c) Mean annual precipitation for the New York City region, (cm), 1970-1999 in each of the 16 1076
GCMs, GCM ensemble, and Central Park observations. Also shown as hash marks is the 1077
interannual standard deviation about the mean for each of the 18 products. d) monthly mean 1078
precipitation for the New York City region, (cm), 1970-1999. Central park observations, the 1079
GCM ensemble average, and four points in the GCM distribution (lowest, 17th percentile, 83rd 1080
percentile, and highest) are shown.
1081 1082 1083
1084 1085 1086
_ 1087
1088
Figure 4: Daily distribution (number of days per year) of: a) all-year mean, b) summer (June-1089
August) maximum, and c) winter (December-February) minimum temperature anomalies (ºC), 1090
1980-1999 for Central Park observations (black line) and three GCMs (CSIRO, GISS, and MPI 1091
ECHAM5).
1092 1093
1095
_ 1096
Figure 5: Daily distribution (number of days per year) of precipitation (mm), 1980-1999 for 1097
Central Park observations (black line) and three GCMs (CSIRO, GISS, and MPI ECHAM5). The 1098
first bin, containing less than 10 mm, is not shown.
1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113
1114
_ 1115
Figure 6: Combined observed (black line) and projected: a) temperature (ºC) and b) annual 1116
precipitation (mm). Projected model changes through time are applied to the observed historical 1117
data. The three thick lines (red, green, and blue) show the ensemble average for each emissions 1118
scenario across the 16 GCMs. Shading shows the central 67 % range across the 16 GCMs and 3 1119
emissions scenarios. The bottom and top lines, respectively, show each year’s minimum and 1120
maximum projections across the suite of simulations. A ten-year filter has been applied to the 1121
observed data and model output. The dotted area between 2003 and 2015 represents the period 1122
that is not covered due to the smoothing procedure.
1123 1124 1125 1126 1127
1129
1130 _ 1131
Figure 7: Seasonal a) temperature change (ºC) and b) precipitation change (%) projections, 1132
relative to the 1970-1999 model baseline, based on 16 GCMs and 3 emissions scenarios. The 1133
maximum and minimum are shown as black horizontal lines; the central 67% of values are 1134
boxed, and the median is the thick line inside the boxes.
1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147
1148 1149
_ 1150
Figure 8: Daily distribution (number of days per year) of winter (December-February) minimum 1151
temperature anomalies (ºC), for the New York Metropolitan Region in the CSIRO GCM. Black 1152
line, 1980-1999 hindcast; dotted line, 2080-2099 A1B scenario.
1153 1154