Climate data: past, present, future Availability and concerns Week 2, Applied Climatology September

1
Climate data past, present, futureAvailability
and concerns Week 2, Applied
ClimatologySeptember 10, 2009
2
Primary data collection

• Primary data can now be collected via relatively
  cheap data loggers or transmitted wirelessly
• Biggest advantages?
• Disadvantages?

Onset Corp.
3
Secondary data collection

• Probably how youll get your data
• Advantages?
• Disadvantages?

4
Hierarchy

• WMO
• NOAA
• National Weather Service
• National Climatic Data Center
• State Climatology offices
• Regional Climate centers

5
Questions about observations

• Is the instrument calibrated properly? (accuracy)
• Is the instrument recording representative data?
  (validity)
• How spatially varying is the phenomenon?
• What is the potential for bias?
• Is the instrument properly sited?
• Is the instrument recoding too coarse data?
  (precision)
• How are observations interpolated?
• Are the data measuring what you want them to?

6
Ideal siting

• Open location with low vegetation
• Horizontal distance of 2 x vertical height of
  nearest object
• No nearby artificial heat sources
• Not in unusual microclimate (e.g. shallow valley)
  or near irrigated land
• Anemometer at 10 m elevation
• Other instruments at 1.5-2 m elevation

7
Siting variability

• Orland, CA
• Marysville, CA
• (surfacestations.org)

8
US Climate Reference Network

• Set up since 2000 to serve as reference point for
  long-term climate records

9
US Historical Climate Network

• Derived from previously observed data
• Many statistical routines run to attempt to
  homogenize datasets

10
Inhomogeneities

• Station move
• Surroundings change
• Instrumentation change

11
Frequency of observations

• Historically, several (3?) times a day of
  instantaneous readings
• Later, also one time per day to get maximum and
  minimum temperature values

(NOAA)
12
Frequency of observations

• Instantaneous values with traces
• Later digital recordings with data loggers
• With geostationary satellites, once per 15 days

13
Types of stations

• First-order station measures primary weather
  variables more or less continuously, reporting
  hourly (at least)
• Second-order station same as first-order, though
  usually less than 24 hour coverage
• Cooperative station usually takes observations
  one time per day

14
Availability of temperature and other primary
meteorological data

• Sub-hourly (mesonet stations)
• Hourly (all first-order stations)
• Daily (first-order, cooperative stations)
• Monthly, Yearly (station, division, or state
  aggregate)

15
Meteorological Assimilation Data Ingest System
(MADIS)

• 35,000 stations

16
Automated Surface Observation System (ASOS)

• Debuted in US in 1990s
• Controls all first-order stations presently

17
ASOS first-order stations

• Report hourly values
• Report sub-hourly only if conditions
  significantly change
• Report maximum/minimum temperature every six
  hours and every day
• Are geared towards aviation purposes

18
Things ASOS does (not) measure

• YES
• Clouds on vertical to 12,000
• Surface visibility and obstructions
• Present weather
• Temperature / dew point
• Pressure / altimeter
• Wind
• Precipitation accumulation
• Significant weather changes

• NO
• Clouds off-vertical or above 12,000
• Variable visibility
• Mixed precipitation
• Lightning
• Tornado
• Snowfall
• Snow on ground

19
Temperature measurements
Thermistor (sunshinelamp.net)
Max/Min Thermometer (Wikipedia)
Infrared Radiation (NASA)
20
Stevenson screen / cotton shelter
21
Station bias example
22
Station bias example
23
Time of observation bias

• 24-hour observations taken at
• Midnight (all first-order stations)
• Early morning (6am-8am) especially farm
  stations
• Evening (6pm-10pm)

24
Mean calculation

• Akron 1W, February 2007
• What is the difference between average mean?
• Monthly mean usually average of daily mean max
  and daily mean min

25
Levels of aggregation

• Individual station

26
Levels of aggregation

• Climate division

27
Levels of aggregation

• State

28
Levels of aggregation

• Region / nation / world

29
Sea surface temperatures
Can be measured via satellite Why is land surface
temperature generally not obtained?
30
Derived variables

• HDD (heating degree days)
• CDD (cooling degree days)
• GDD (growing degree days)
• etc

31
Precipitation measurements
Weighing gauge (NOAA)
Tipping bucket (Wikipedia)
Standard gauge (Wikipedia)
Radar (NOAA)
32
Availability of precipitation data

• 1-minute and 5-minute
• Hourly
• Daily
• Monthly
• Issues with snow?

33
Radar estimates of precipitation

• Produced in 1 hour and storm total maps
• Can be compromised by hail and sleet
• Eastern US Radar estimates corrected by ground
  observations
• Western US Long-term climatological
  interpolations done

34
Aggregation for precipitation
35
Aggregation for precipitation

• How is this different from temperature?

36
Aggregation for precipitation
37
Other variables

• Relative humidity / dew point
• Wind direction and speed
• Cloud cover
• Atmospheric pressure
• Snowfall
• Lightning

38
Lightning detection

• Detect electrical discharge through several
  sensors
• Triangulate location and polarity

39
The lesser variables

• Sunshine duration
• Radiation (only 221 complete stations in US)
• Evaporation
• Soil temperature (only 2 in Ohio)

40
Pan evaporation / lysimiter
USDA
41
Indirect variables

• River flow
• Ice cover

42
Storm Data / Storm Reports

• Drought
• Dust storm
• Flood
• Fog
• Hail
• Hurricane
• Lightning
• Ocean surf

• Precipitation
• Snow / Ice
• Temperature extremes
• Tornado
• Wildfire
• Wind

43
Daily synoptic series
NOAA
44
Upper air observations

• Radiosonde
• Developed in 1928 flourished since WW2
• Temperature, humidity, pressure
• Rawinsonde
• Similar, though provides wind speed as well
• Wind profilers
• Measure from ground

45
Upper air observation locations
46
Reanalysis data

• Combination of weather forecast model
  initialization and analysis, and short-term
  forecast
• Project started in 1990s to reproduce synoptic
  maps back to 1948 extrapolation to 1908 coming
  soon
• Two significant programs
• NCEP / NCAR NNR (USA)
• ECMWF ERA (European Union)

47
Reanalysis example
48
Reanalysis fields produced

• Class A the most reliable class of variables
  "analysis variable is strongly influenced by
  observed data"
• Class B the next most reliable class of
  variables "although some observational data
  directly affect the value of the variable, the
  model also has a very strong influence on the
  output values."
• Class C the least reliable class of variables
  NO observations directly affect the variable and
  it is derived solely from the model computations
  forced by the model's data assimilation process,
  not by any real data.
• Class D a mean field that is obtained from
  climatological values and does not depend on the
  model

49
Climate projections

• Run like weather forecast models except for what
  main differences?

50
Time and space with weather climate models

• Horizontal grid spacing
• Weather 5-40 km
• Climate 100-300 km
• Vertical layers
• Weather 30-60
• Climate 19
• Time step
• Weather 6 minutes
• Climate 30-60 minutes

51
Horizontal resolution
52
What climate models calculate

• Prognostic variables
• Fundamental variables including pressure, layer
  temperature, vorticity, divergence
• Diagnostic variables
• Calculated from prognostic variables, such as
  surface (1.5m) temperature, vertical motion
• Parameterizations
• Simulates processes that occur on scales too
  small for a gridbox, such as cumulus clouds

53
Types of forecasts

• Steady state
• Double the CO2 level of the pre-industrial era
• Transient run
• Gradual increase in CO2- usually assumed to be
  1/year
• Scenarios
• Increases in CO2 depend on numerous factors

54
Scenarios

• SRES scenarios
• Economic vs. Environmental
• Global vs. Regional
• Energy sources
• IS92 scenarios
• 6 different levels of population growth, economic
  growth, and fossil fuel use

55
Scenarios
56
Scenarios

• CO2 equivalent other GHGs are converted into
  CO2 based on radiative properties

57
Climate models

• Best handle on atmospheric pressure
• Temperature good, except near poles
• Moisture, Cloudiness, Precipitation much weaker
• Errors can be
• Systematic (global under/overestimation)
• Unsystematic (precipitation placed in wrong
  location, or not developed at all)
• Of variance (too much/too little variability)

58
Climate model predictions

• Vary by
• Scenario used
• Model used
• Location on globe
• Usually checked against 20th century climate
• Representations worst near polar areas

59
Climate model issuesDownscaling

• Where a grid box includes areas that are
  significantly different (e.g. Southern
  California), surface conditions may be difficult
  to interpret. Modelers can then
• Run Regional Climate Models (RCM)
• Statistically downscale from the grid mean